@article {69756, title = {Periodic ER-plasma membrane junctions support long-range Ca signal integration in dendrites.}, journal = {Cell}, volume = {188}, year = {2025}, month = {01/2025}, pages = {484-500.e22}, abstract = {

Neuronal dendrites must relay synaptic inputs over long distances, but the mechanisms by which activity-evoked intracellular signals propagate over macroscopic distances remain unclear. Here, we discovered a system of periodically arranged endoplasmic reticulum-plasma membrane (ER-PM) junctions tiling the plasma membrane of dendrites at \~{}1\ μm intervals, interlinked by a meshwork of ER tubules patterned in a ladder-like array. Populated with Junctophilin-linked plasma membrane voltage-gated Ca channels and ER Ca-release channels (ryanodine receptors), ER-PM junctions are hubs for ER-PM crosstalk, fine-tuning of Ca homeostasis, and local activation of the Ca/calmodulin-dependent protein kinase II. Local spine stimulation activates the Ca modulatory machinery, facilitating signal transmission and ryanodine-receptor-dependent Ca release at ER-PM junctions over 20\ μm away. Thus, interconnected ER-PM junctions support signal propagation and Ca release from the spine-adjacent ER. The capacity of this subcellular architecture to modify both local and distant membrane-proximal biochemistry potentially contributes to dendritic computations.

}, keywords = {Animals, Calcium, Calcium Signaling, Calcium-Calmodulin-Dependent Protein Kinase Type 2, Cell Membrane, Dendrites, Endoplasmic Reticulum, Hippocampus, Humans, Male, Mice, Rats, Ryanodine Receptor Calcium Release Channel}, issn = {1097-4172}, doi = {10.1016/j.cell.2024.11.029}, author = {Benedetti, Lorena and Fan, Ruolin and Weigel, Aubrey V and Moore, Andrew S and Houlihan, Patrick R and Kittisopikul, Mark and Park, Grace and Petruncio, Alyson and Hubbard, Philip M and Pang, Song and Xu, C Shan and Hess, Harald F and Saalfeld, Stephan and Rangaraju, Vidhya and Clapham, David E and De Camilli, Pietro and Ryan, Timothy A and Lippincott-Schwartz, Jennifer} } @article {70155, title = {Sexual dimorphism in the complete connectome of the Drosophila male central nervous system}, journal = {bioRxiv}, year = {2025}, month = {2025 Oct 09}, abstract = {

Sex differences in behaviour exist across the animal kingdom, typically under strong genetic regulation. In Drosophila, previous work has shown that fruitless and doublesex transcription factors identify neurons driving sexually dimorphic behaviour. However, the organisation of dimorphic neurons into functional circuits remains unclear.We now present the connectome of the entire Drosophila male central nervous system. This contains 166,691 neurons spanning the brain and ventral nerve cord, fully proofread and comprehensively annotated including fruitless and doublesex expression and 11,691 cell types. By comparison with a previous female brain connectome, we provide the first comprehensive description of the differences between male and female brains to synaptic resolution. Of 7,319 cross-matched cell types in the central brain, 114 are dimorphic with an additional 262 male- and 69 female-specific (totalling 4.8\% of neurons in males and 2.4\% in females).This resource enables analysis of full sensory-to-motor circuits underlying complex behaviours as well as the impact of dimorphic elements. Sex-specific and dimorphic neurons are concentrated in higher brain centres while the sensory and motor periphery are largely isomorphic. Within higher centres, male-specific connections are organised into hotspots defined by male-specific neurons or the presence of male-specific arbours on neurons that are otherwise similar between sexes. Numerous circuit switches reroute sensory information to form conserved, antagonistic circuits controlling opposing behaviours.

}, doi = {10.1101/2025.10.09.680999}, url = {https://www.biorxiv.org/content/early/2025/10/09/2025.10.09.680999}, author = {Berg, Stuart and Beckett, Isabella R and Costa, Marta and Schlegel, Philipp and Januszewski, Micha{\l} and Marin, Elizabeth C and Nern, Aljoscha and Preibisch, Stephan and Qiu, Wei and Takemura, Shin-ya and Fragniere, Alexandra MC and Champion, Andrew S and Adjavon, Diane-Yayra and Cook, Michael and Gkantia, Marina and Hayworth, Kenneth J and Huang, Gary B and Katz, William T and K{\"a}mpf, Florian and Lu, Zhiyuan and Ordish, Christopher and Paterson, Tyler and St{\"u}rner, Tomke and Trautman, Eric T and Whittle, Catherine R and Burnett, Laura E and Hoeller, Judith and Li, Feng and Loesche, Frank and Morris, Billy J and Pietzsch, Tobias and Pleijzier, Markus W and Silva, Valeria and Yin, Yijie and Ali, Iris and Badalamente, Griffin and Bates, Alexander Shakeel and Bogovic, John and Brooks, Paul and Cachero, Sebastian and Canino, Brandon S and Chaisrisawatsuk, Bhumpanya and Clements, Jody and Crowe, Arthur and de Haan Vicente, In{\^e}s and Dempsey, Georgia and Don{\`a}, Erika and dos Santos, M{\'a}rcia and Dreher, Marisa and Dunne, Christopher R and Eichler, Katharina and Finley-May, Samantha and Flynn, Miriam A and Hameed, Imran and Hopkins, Gary Patrick and Hubbard, Philip M and Kiassat, Ladann and Kovalyak, Julie and Lauchie, Shirley A and Leonard, Meghan and Lohff, Alanna and Longden, Kit D and Maldonado, Charli A and Mitletton, Myrto and Moitra, Ilina and Moon, Sung Soo and Mooney, Caroline and Munnelly, Eva J and Okeoma, Nneoma and Olbris, Donald J and Pai, Anika and Patel, Birava and Phillips, Emily M and Plaza, Stephen M and Richards, Alana and Rivas Salinas, Jennifer and Roberts, Ruair{\'\i} Jv and Rogers, Edward M and Scott, Ashley L and Scuderi, Louis A and Seenivasan, Pavithraa and Serratosa Capdevila, Laia and Smith, Claire and Svirskas, Rob and Takemura, Satoko and Tastekin, Ibrahim and Thomson, Alexander and Umayam, Lowell and Walsh, John J and Whittome, Holly and Xu, C Shan and Yakal, Emily A and Yang, Tansy and Zhao, Arthur and George, Reed and Jain, Viren and Jayaraman, Vivek and Korff, Wyatt and Meissner, Geoffrey W and Romani, Sandro and Funke, Jan and Knecht, Christopher and Saalfeld, Stephan and Scheffer, Louis K and Waddell, Scott and Card, Gwyneth M and Ribeiro, Carlos and Reiser, Michael B and Hess, Harald F and Rubin, Gerald M and Jefferis, Gregory Sxe} } @article {69602, title = {Data Release: High-Resolution Imaging and Segmentation of P7 Mouse Tissue Microarchitecture Using FIB-SEM and Machine Learning}, journal = {bioRxiv}, year = {2024}, month = {2024 Sep 07}, abstract = {

This report presents a comprehensive data release exploring the tissue microarchitecture of P7 aged mice using Focused Ion Beam Scanning Electron Microscopy (FIB-SEM) combined with machine learning-based segmentations of nuclei. The study includes high-resolution 3D volumes and nucleus segmentations for seven vital tissues{\textemdash}pancreas, liver, kidney, heart, thymus, hippocampus, and skin{\textemdash}from a single mouse. The detailed datasets are openly accessible on OpenOrganelle.org, providing a valuable resource for the scientific community to support further research and collaboration.

}, doi = {10.1101/2024.09.05.611438}, url = {https://www.doi.org/10.1101/2024.09.05.611438}, author = {Ackerman, David and Avetissian, Emma and Bleck, Christopher K. E. and Bogovic, John A. and Innerberger, Michael and Korff, Wyatt and Li, Wei-Ping and Lu, Zhiyuan and Petruncio, Alyson and Preibisch, Stephan and Qiu, Wei and Jeff L Rhoades and Saalfeld, Stephan and Silva, Malan and Trautman, Eric T. and Vorimo, Rebecca and Weigel, Aubrey and Yu, Zhiheng and Zubov, Yurii and ,} } @article {69567, title = {Decomposing heterogeneous dynamical systems with graph neural networks}, journal = {arXiv}, year = {2024}, month = {2024 Jul 27}, abstract = {

Natural physical, chemical, and biological dynamical systems are often complex, with heterogeneous components interacting in diverse ways. We show that graph neural networks can be designed to jointly learn the interaction rules and the structure of the heterogeneity from data alone. The learned latent structure and dynamics can be used to virtually decompose the complex system which is necessary to parameterize and infer the underlying governing equations. We tested the approach with simulation experiments of moving particles and vector fields that interact with each other. While our current aim is to better understand and validate the approach with simulated data, we anticipate it to become a generally applicable tool to uncover the governing rules underlying complex dynamics observed in nature.

}, doi = {10.48550/arXiv.2407.19160}, url = {https://doi.org/10.48550/arXiv.2407.19160}, author = {Allier, C{\'e}dric and Schneider, Magdalena C and Innerberger, Michael and Heinrich, Larissa and Bogovic, John A and Saalfeld, Stephan} } @article {69704, title = {Periodic ER-plasma membrane junctions support long-range Ca signal integration in dendrites.}, journal = {Cell}, year = {2024}, month = {2024 Dec 18}, abstract = {

Neuronal dendrites must relay synaptic inputs over long distances, but the mechanisms by which activity-evoked intracellular signals propagate over macroscopic distances remain unclear. Here, we discovered a system of periodically arranged endoplasmic reticulum-plasma membrane (ER-PM) junctions tiling the plasma membrane of dendrites at \~{}1\ μm intervals, interlinked by a meshwork of ER tubules patterned in a ladder-like array. Populated with Junctophilin-linked plasma membrane voltage-gated Ca channels and ER Ca-release channels (ryanodine receptors), ER-PM junctions are hubs for ER-PM crosstalk, fine-tuning of Ca homeostasis, and local activation of the Ca/calmodulin-dependent protein kinase II. Local spine stimulation activates the Ca modulatory machinery, facilitating signal transmission and ryanodine-receptor-dependent Ca release at ER-PM junctions over 20\ μm away. Thus, interconnected ER-PM junctions support signal propagation and Ca release from the spine-adjacent ER. The capacity of this subcellular architecture to modify both local and distant membrane-proximal biochemistry potentially contributes to dendritic computations.

}, issn = {1097-4172}, doi = {10.1016/j.cell.2024.11.029}, author = {Benedetti, Lorena and Fan, Ruolin and Weigel, Aubrey V and Moore, Andrew S and Houlihan, Patrick R and Kittisopikul, Mark and Park, Grace and Petruncio, Alyson and Hubbard, Philip M and Pang, Song and Xu, C Shan and Hess, Harald F and Saalfeld, Stephan and Rangaraju, Vidhya and Clapham, David E and De Camilli, Pietro and Ryan, Timothy A and Lippincott-Schwartz, Jennifer} } @article {69139, title = {Petascale pipeline for precise alignment of images from serial section electron microscopy.}, journal = {Nature Communications}, volume = {15}, year = {2024}, month = {2024 Jan 04}, pages = {289}, abstract = {

The reconstruction of neural circuits from serial section electron microscopy (ssEM) images is being accelerated by automatic image segmentation methods. Segmentation accuracy is often limited by the preceding step of aligning 2D section images to create a 3D image stack. Precise and robust alignment in the presence of image artifacts is challenging, especially as datasets are attaining the petascale. We present a computational pipeline for aligning ssEM images with several key elements. Self-supervised convolutional nets are trained via metric learning to encode and align image pairs, and they are used to initialize iterative fine-tuning of alignment. A procedure called vector voting increases robustness to image artifacts or missing image data. For speedup the series is divided into blocks that are distributed to computational workers for alignment. The blocks are aligned to each other by composing transformations with decay, which achieves a global alignment without resorting to a time-consuming global optimization. We apply our pipeline to a whole fly brain dataset, and show improved accuracy relative to prior state of the art. We also demonstrate that our pipeline scales to a cubic millimeter of mouse visual cortex. Our pipeline is publicly available through two open source Python packages.

}, keywords = {Animals, Brain, Image Processing, Computer-Assisted, Imaging, Three-Dimensional, Mice, Microscopy, Electron}, issn = {2041-1723}, doi = {10.1038/s41467-023-44354-0}, author = {Popovych, Sergiy and Macrina, Thomas and Kemnitz, Nico and Castro, Manuel and Nehoran, Barak and Jia, Zhen and Bae, J Alexander and Mitchell, Eric and Mu, Shang and Trautman, Eric T and Saalfeld, Stephan and Li, Kai and Seung, H Sebastian} } @article {68807, title = {Architecture and dynamics of a desmosome-endoplasmic reticulum complex.}, journal = {Nature Cell Biology}, volume = {25}, year = {2023}, month = {2023 Jun 16}, pages = {823-835}, abstract = {

The endoplasmic reticulum (ER) forms a dynamic network that contacts other cellular membranes to regulate stress responses, calcium signalling and lipid transfer. Here, using high-resolution volume electron microscopy, we find that the ER forms a previously unknown association with keratin intermediate filaments and desmosomal cell-cell junctions. Peripheral ER assembles into mirror image-like arrangements at desmosomes and exhibits nanometre proximity to keratin filaments and the desmosome cytoplasmic plaque. ER tubules exhibit stable associations with desmosomes, and perturbation of desmosomes or keratin filaments alters ER organization, mobility and expression of ER stress transcripts. These findings indicate that desmosomes and the keratin cytoskeleton regulate the distribution, function and dynamics of the ER network. Overall, this study reveals a previously unknown subcellular architecture defined by the structural integration of ER tubules with an epithelial intercellular junction.

}, keywords = {Cytoskeleton, Desmosomes, Endoplasmic Reticulum, Intermediate Filaments, Keratins}, issn = {1476-4679}, doi = {10.1038/s41556-023-01154-4}, author = {Bharathan, Navaneetha Krishnan and Giang, William and Hoffman, Coryn L and Aaron, Jesse S and Khuon, Satya and Chew, Teng-Leong and Preibisch, Stephan and Trautman, Eric T and Heinrich, Larissa and Bogovic, John and Bennett, Davis and Ackerman, David and Park, Woohyun and Petruncio, Alyson and Weigel, Aubrey V and Saalfeld, Stephan and Wayne Vogl, A and Stahley, Sara N and Kowalczyk, Andrew P} } @article {68805, title = {Comparative connectomics and escape behavior in larvae of closely related Drosophila species.}, journal = {Current Biology}, year = {2023}, month = {2023 May 31}, abstract = {

Evolution has generated an enormous variety of morphological, physiological, and behavioral traits in animals. How do behaviors evolve in different directions in species equipped with similar neurons and molecular components? Here we adopted a comparative approach to investigate the similarities and differences of escape behaviors in response to noxious stimuli and their underlying neural circuits between closely related drosophilid species. Drosophilids show a wide range of escape behaviors in response to noxious cues, including escape crawling, stopping, head casting, and rolling. Here we find that D.\ santomea, compared with its close relative D.\ melanogaster, shows a higher probability of rolling in response to noxious stimulation. To assess whether this behavioral difference could be attributed to differences in neural circuitry, we\ generated focused ion beam-scanning electron microscope volumes of the ventral nerve cord of D.\ santomea to reconstruct the downstream partners of mdIV, a nociceptive sensory neuron in D.\ melanogaster. Along with partner interneurons of mdVI (including Basin-2, a multisensory integration neuron necessary for rolling) previously identified in D.\ melanogaster, we identified two additional partners of mdVI in D.\ santomea. Finally, we showed that joint activation of one of the partners (Basin-1) and a common partner (Basin-2) in D.\ melanogaster increased rolling probability, suggesting that the high rolling probability in D.\ santomea is mediated by the additional activation of Basin-1 by mdIV. These results provide a plausible mechanistic explanation for how closely related species exhibit quantitative differences in the likelihood of expressing the same behavior.

}, issn = {1879-0445}, doi = {10.1016/j.cub.2023.05.043}, author = {Zhu, Jiayi and Boivin, Jean-Christophe and Pang, Song and Xu, C Shan and Lu, Zhiyuan and Saalfeld, Stephan and Hess, Harald F and Ohyama, Tomoko} } @article {68564, title = {Local shape descriptors for neuron segmentation.}, journal = {Nature Methods}, volume = {20}, year = {2023}, month = {2023 Feb 01}, pages = {295-303}, abstract = {

We present an auxiliary learning task for the problem of neuron segmentation in electron microscopy volumes. The auxiliary task consists of the prediction of local shape descriptors (LSDs), which we combine with conventional voxel-wise direct neighbor affinities for neuron boundary detection. The shape descriptors capture local statistics about the neuron to be segmented, such as diameter, elongation, and direction. On a study comparing several existing methods across various specimen, imaging techniques, and resolutions, auxiliary learning of LSDs consistently increases segmentation accuracy of affinity-based methods over a range of metrics. Furthermore, the addition of LSDs promotes affinity-based segmentation methods to be on par with the current state of the art for neuron segmentation (flood-filling networks), while being two orders of magnitudes more efficient-a critical requirement for the processing of future petabyte-sized datasets.

}, issn = {1548-7105}, doi = {10.1038/s41592-022-01711-z}, author = {Sheridan, Arlo and Nguyen, Tri M and Deb, Diptodip and Lee, Wei-Chung Allen and Saalfeld, Stephan and Turaga, Srinivas C and Manor, Uri and Funke, Jan} } @article {68958, title = {Towards Generalizable Organelle Segmentation in Volume Electron Microscopy.}, journal = {Microscopy and Microanalysis}, volume = {29}, year = {2023}, month = {2023 Jul 22}, pages = {975}, keywords = {Organelles, Volume Electron Microscopy}, issn = {1435-8115}, doi = {10.1093/micmic/ozad067.487}, author = {Heinrich, Larissa and Patton, Will and Bennett, Davis and Ackerman, David and Park, Grace and Bogovic, John A and Eckstein, Nils and Petruncio, Alyson and Clements, Jody and Pang, Song and Shan Xu, C and Funke, Jan and Korff, Wyatt and Hess, Harald and Lippincott-Schwartz, Jennifer and Saalfeld, Stephan and Weigel, Aubrey} } @article {68251, title = {A scalable and modular automated pipeline for stitching of large electron microscopy datasets.}, journal = {eLife}, volume = {11}, year = {2022}, month = {2022 Jul 26}, abstract = {

Serial-section electronmicroscopy (ssEM) is themethod of choice for studyingmacroscopic biological samples at extremely high resolution in three dimensions. In the nervous system, nanometer-scale images are necessary to reconstruct dense neural wiring diagrams in the brain, so called connectomes. In order to use this data, consisting of up to 10 individual EM images, it must be assembled into a volume, requiring seamless 2D stitching from each physical section followed by 3D alignment of the stitched sections. The high throughput of ssEM necessitates 2D stitching to be done at the pace of imaging, which currently produces tens of terabytes per day. To achieve this, we present a modular volume assembly software pipeline ASAP (Assembly Stitching and Alignment Pipeline) that is scalable to datasets containing petabytes of data and parallelized to work in a distributed computational environment. The pipeline is built on top of the Render (27) services used in the volume assembly of the brain of adult Drosophilamelanogaster (30). It achieves high throughput by operating on themeta-data and transformations of each image stored in a database, thus eliminating the need to render intermediate output. ASAP ismodular, allowing for easy incorporation of new algorithms without significant changes in the workflow. The entire software pipeline includes a complete set of tools for stitching, automated quality control, 3D section alignment, and final rendering of the assembled volume to disk. ASAP has been deployed for continuous stitching of several large-scale datasets of the mouse visual cortex and human brain samples including one cubic millimeter of mouse visual cortex (28; 8) at speeds that exceed imaging. The pipeline also has multi-channel processing capabilities and can be applied to fluorescence and multi-modal datasets like array tomography.

}, issn = {2050-084X}, doi = {10.7554/eLife.76534}, author = {Mahalingam, Gayathri and Torres, Russel and Kapner, Daniel and Trautman, Eric T and Fliss, Tim and Seshamani, Shamishtaa and Perlman, Eric and Young, Rob and Kinn, Samuel and Buchanan, JoAnn and Takeno, Marc M and Yin, Wenjing and Bumbarger, Daniel J and Gwinn, Ryder P and Nyhus, Julie and Lein, Ed and Smith, Steven J and Reid, R Clay and Khairy, Khaled A and Saalfeld, Stephan and Collman, Forrest and Macarico da Costa, Nuno} } @article {67988, title = {Transverse endoplasmic reticulum expansion in hereditary spastic paraplegia corticospinal axons.}, journal = {Human Molecular Genetics}, year = {2022}, month = {2022 Mar 26}, abstract = {

Hereditary spastic paraplegias (HSPs) comprise a large group of inherited neurologic disorders affecting the longest corticospinal axons (SPG1-86 plus others), with shared manifestations of lower extremity spasticity and gait impairment. Common autosomal dominant HSPs are caused by mutations in genes encoding the microtubule-severing ATPase spastin (SPAST; SPG4), the membrane-bound GTPase atlastin-1 (ATL1; SPG3A), and the reticulon-like, microtubule-binding protein REEP1 (REEP1; SPG31). These proteins bind one another and function in shaping the tubular endoplasmic reticulum (ER) network. Typically, mouse models of HSPs have mild, later-onset phenotypes, possibly reflecting far shorter lengths of their corticospinal axons relative to humans. Here, we have generated a robust, double mutant mouse model of HSP in which atlastin-1 is genetically modified with a K80A knock-in (KI) missense change that abolishes its GTPase activity, while its binding partner Reep1 is knocked out. Atl1KI/KI/Reep1-/- mice exhibit early-onset and rapidly progressive declines in several motor function tests. Also, ER in mutant corticospinal axons dramatically expands transversely and periodically in a mutation dosage-dependent manner to create a ladder-like appearance, based on reconstructions of focused ion beam-scanning electron microscopy datasets using machine learning-based auto-segmentation. In lockstep with changes in ER morphology, axonal mitochondria are fragmented and proportions of hypophosphorylated neurofilament H and M subunits are dramatically increased in Atl1KI/KI/Reep1-/- spinal cord. Co-occurrence of these findings links ER morphology changes to alterations in mitochondrial morphology and cytoskeletal organization. Atl1KI/KI/Reep1-/- mice represent an early-onset rodent HSP model with robust behavioral and cellular readouts for testing novel therapies.

}, issn = {1460-2083}, doi = {10.1093/hmg/ddac072}, author = {Zhu, Peng-Peng and Hung, Hui-Fang and Batchenkova, Natalia and Nixon-Abell, Jonathon and Henderson, James and Zheng, Pengli and Renvois{\'e}, Benoit and Pang, Song and Xu, C Shan and Saalfeld, Stephan and Funke, Jan and Xie, Yuxiang and Svara, Fabian and Hess, Harald F and Blackstone, Craig} } @article {68298, title = {Transverse endoplasmic reticulum expansion in hereditary spastic paraplegia corticospinal axons.}, journal = {Human Molecular Genetics}, volume = {31}, year = {2022}, month = {2022 Aug 23}, pages = {2779-2795}, abstract = {

Hereditary spastic paraplegias (HSPs) comprise a large group of inherited neurologic disorders affecting the longest corticospinal axons (SPG1-86 plus others), with shared manifestations of lower extremity spasticity and gait impairment. Common autosomal dominant HSPs are caused by mutations in genes encoding the microtubule-severing ATPase spastin (SPAST; SPG4), the membrane-bound GTPase atlastin-1 (ATL1; SPG3A) and the reticulon-like, microtubule-binding protein REEP1 (REEP1; SPG31). These proteins bind one another and function in shaping the tubular endoplasmic reticulum (ER) network. Typically, mouse models of HSPs have mild, later onset phenotypes, possibly reflecting far shorter lengths of their corticospinal axons relative to humans. Here, we have generated a robust, double mutant mouse model of HSP in which atlastin-1 is genetically modified with a K80A knock-in (KI) missense change that abolishes its GTPase activity, whereas its binding partner Reep1 is knocked out. Atl1KI/KI/Reep1-/- mice exhibit early onset and rapidly progressive declines in several motor function tests. Also, ER in mutant corticospinal axons dramatically expands transversely and periodically in a mutation dosage-dependent manner to create a ladder-like appearance, on the basis of reconstructions of focused ion beam-scanning electron microscopy datasets using machine learning-based auto-segmentation. In lockstep with changes in ER morphology, axonal mitochondria are fragmented and proportions of hypophosphorylated neurofilament H and M subunits are dramatically increased in Atl1KI/KI/Reep1-/- spinal cord. Co-occurrence of these findings links ER morphology changes to alterations in mitochondrial morphology and cytoskeletal organization. Atl1KI/KI/Reep1-/- mice represent an early onset rodent HSP model with robust behavioral and cellular readouts for testing novel therapies.

}, keywords = {Animals, Axons, Disease Models, Animal, Endoplasmic Reticulum, GTP Phosphohydrolases, Humans, Membrane Proteins, Membrane Transport Proteins, Mice, Mice, Knockout, Mutation, Spastic Paraplegia, Hereditary, Spastin}, issn = {1460-2083}, doi = {10.1093/hmg/ddac072}, author = {Zhu, Peng-Peng and Hung, Hui-Fang and Batchenkova, Natalia and Nixon-Abell, Jonathon and Henderson, James and Zheng, Pengli and Renvois{\'e}, Benoit and Pang, Song and Xu, C Shan and Saalfeld, Stephan and Funke, Jan and Xie, Yuxiang and Svara, Fabian and Hess, Harald F and Blackstone, Craig} } @article {65832, title = {Automatic Detection of Synaptic Partners in a Whole-Brain Drosophila EM Dataset}, journal = {Nature Methods}, volume = {18}, year = {2021}, month = {2021 Jul 1}, pages = {771-4}, abstract = {

The study of neural circuits requires the reconstruction of neurons and the identification of synaptic connections between them. To scale the reconstruction to the size of whole-brain datasets, semi-automatic methods are needed to solve those tasks. Here, we present an automatic method for synaptic partner identification in insect brains, which uses convolutional neural networks to identify post-synaptic sites and their pre-synaptic partners. The networks can be trained from human generated point annotations alone and requires only simple post-processing to obtain final predictions. We used our method to extract 244 million putative synaptic partners in the fifty-teravoxel full adult fly brain (FAFB) electron microscopy (EM) dataset and evaluated its accuracy on 146,643 synapses from 702 neurons with a total cable length of 312 mm in four different brain regions. The predicted synaptic connections can be used together with a neuron segmentation to infer a connectivity graph with high accuracy: 96\% of edges between connected neurons are correctly classified as weakly connected (less than five synapses) and strongly connected (at least five synapses). Our synaptic partner predictions for the FAFB dataset are publicly available, together with a query library allowing automatic retrieval of up- and downstream neurons.

}, doi = {10.1038/s41592-021-01183-7}, author = {Buhmann, Julia and Sheridan, Arlo and Gerhard, Stephan and Krause, Renate and Nguyen, Tri and Heinrich, Larissa and Schlegel, Philipp and Lee, Wei-Chung Allen and Wilson, Rachel and Saalfeld, Stephan and Jefferis, Gregory and Bock, Davi and Turaga, Srinivas and Cook, Matthew and Funke, Jan} } @article {67815, title = {EASI-FISH for thick tissue defines lateral hypothalamus spatio-molecular organization.}, journal = {Cell}, volume = {184}, year = {2021}, month = {2021 Dec 01}, pages = {6361}, abstract = {

Determining the spatial organization and morphological characteristics of molecularly defined cell types is a major bottleneck for characterizing the architecture underpinning brain function. We developed Expansion-Assisted Iterative Fluorescence In Situ Hybridization (EASI-FISH) to survey gene expression in brain tissue, as well as a turnkey computational pipeline to rapidly process large EASI-FISH image datasets. EASI-FISH was optimized for thick brain sections (300\ μm) to facilitate reconstruction of spatio-molecular domains that generalize across brains. Using the EASI-FISH pipeline, we investigated the spatial distribution of dozens of molecularly defined cell types in the lateral hypothalamic area (LHA), a brain region with poorly defined anatomical organization. Mapping cell types in the LHA revealed nine spatially and molecularly defined subregions. EASI-FISH also facilitates iterative reanalysis of scRNA-seq datasets to determine marker-genes that further dissociated spatial and morphological heterogeneity. The EASI-FISH pipeline democratizes mapping molecularly defined cell types, enabling discoveries about brain organization.

}, issn = {1097-4172}, doi = {10.1016/j.cell.2021.11.024}, author = {Wang, Yuhan and Eddison, Mark and Fleishman, Greg and Weigert, Martin and Xu, Shengjin and Wang, Tim and Rokicki, Konrad and Goina, Cristian and Henry, Fredrick E and Lemire, Andrew L and Schmidt, Uwe and Yang, Hui and Svoboda, Karel and Myers, Eugene W and Saalfeld, Stephan and Korff, Wyatt and Sternson, Scott M and Tillberg, Paul W} } @article {67228, title = {Local Shape Descriptors for Neuron Segmentation}, journal = {bioRxiv}, year = {2021}, month = {2021/01}, abstract = {

We present a simple, yet effective, auxiliary learning task for the problem of neuron segmentation in electron microscopy volumes. The auxiliary task consists of the prediction of Local Shape Descriptors (LSDs), which we combine with conventional voxel-wise direct neighbor affinities for neuron boundary detection. The shape descriptors are designed to capture local statistics about the neuron to be segmented, such as diameter, elongation, and direction. On a large study comparing several existing methods across various specimen, imaging techniques, and resolutions, we find that auxiliary learning of LSDs consistently increases segmentation accuracy of affinity-based methods over a range of metrics. Furthermore, the addition of LSDs promotes affinitybased segmentation methods to be on par with the current state of the art for neuron segmentation (Flood-Filling Networks, FFN), while being two orders of magnitudes more efficient{\textemdash}a critical requirement for the processing of future petabyte-sized datasets. Implementations of the new auxiliary learning task, network architectures, training, prediction, and evaluation code, as well as the datasets used in this study are publicly available as a benchmark for future method contributions.Competing Interest StatementThe authors have declared no competing interest.

}, doi = {10.1101/2021.01.18.427039}, url = {https://www.biorxiv.org/content/early/2021/01/18/2021.01.18.427039}, author = {Sheridan, Arlo and Nguyen, Tri and Deb, Diptodip and Lee, Wei-Chung Allen and Saalfeld, Stephan and Turaga, Srini and Manor, Uri and Funke, Jan} } @article {67750, title = {Whole-cell organelle segmentation in volume electron microscopy.}, journal = {Nature}, volume = {599}, year = {2021}, month = {2021 Nov 01}, pages = {141-46}, abstract = {

Cells contain hundreds of organelles and macromolecular assemblies. Obtaining a complete understanding of their intricate organization requires the nanometre-level, three-dimensional reconstruction of whole cells, which is only feasible with robust and scalable automatic methods. Here, to support the development of such methods, we annotated up to 35 different cellular organelle classes-ranging from endoplasmic reticulum to microtubules to ribosomes-in diverse sample volumes from multiple cell types imaged at a near-isotropic resolution of 4 nm per voxel with focused ion beam scanning electron microscopy (FIB-SEM). We trained deep learning architectures to segment these structures in 4 nm and 8 nm per voxel FIB-SEM volumes, validated their performance and showed that automatic reconstructions can be used to directly quantify previously inaccessible metrics including spatial interactions between cellular components. We also show that such reconstructions can be used to automatically register light and electron microscopy images for correlative studies. We have created an open data and open-source web repository, {\textquoteright}OpenOrganelle{\textquoteright}, to share the data, computer code and trained models, which will enable scientists everywhere to query and further improve automatic reconstruction of these datasets.

}, issn = {1476-4687}, doi = {10.1038/s41586-021-03977-3}, author = {Heinrich, Larissa and Bennett, Davis and Ackerman, David and Park, Woohyun and Bogovic, John and Eckstein, Nils and Petruncio, Alyson and Clements, Jody and Pang, Song and Xu, C Shan and Funke, Jan and Korff, Wyatt and Hess, Harald F and Lippincott-Schwartz, Jennifer and Saalfeld, Stephan and Weigel, Aubrey V} } @article {68507, title = {A connectome and analysis of the adult Drosophila central brain.}, journal = {Elife}, volume = {9}, year = {2020}, month = {2020 Sep 07}, abstract = {

The neural circuits responsible for animal behavior remain largely unknown. We summarize new methods and present the circuitry of a large fraction of the brain of the fruit fly . Improved methods include new procedures to prepare, image, align, segment, find synapses in, and proofread such large data sets. We define cell types, refine computational compartments, and provide an exhaustive atlas of cell examples and types, many of them novel. We provide detailed circuits consisting of neurons and their chemical synapses for most of the central brain. We make the data public and simplify access, reducing the effort needed to answer circuit questions, and provide procedures linking the neurons defined by our analysis with genetic reagents. Biologically, we examine distributions of connection strengths, neural motifs on different scales, electrical consequences of compartmentalization, and evidence that maximizing packing density is an important criterion in the evolution of the fly{\textquoteright}s brain.

}, keywords = {Animals, Brain, Connectome, Drosophila melanogaster, Female, Male, Neurons, Synapses}, issn = {2050-084X}, doi = {10.7554/eLife.57443}, author = {Scheffer, Louis K and Xu, C Shan and Januszewski, Michal and Lu, Zhiyuan and Takemura, Shin-ya and Hayworth, Kenneth J and Huang, Gary B and Shinomiya, Kazunori and Maitlin-Shepard, Jeremy and Berg, Stuart and Clements, Jody and Hubbard, Philip M and Katz, William T and Umayam, Lowell and Zhao, Ting and Ackerman, David and Blakely, Tim and Bogovic, John and Dolafi, Tom and Kainmueller, Dagmar and Kawase, Takashi and Khairy, Khaled A and Leavitt, Laramie and Li, Peter H and Lindsey, Larry and Neubarth, Nicole and Olbris, Donald J and Otsuna, Hideo and Trautman, Eric T and Ito, Masayoshi and Bates, Alexander S and Goldammer, Jens and Wolff, Tanya and Svirskas, Robert and Schlegel, Philipp and Neace, Erika and Knecht, Christopher J and Alvarado, Chelsea X and Bailey, Dennis A and Ballinger, Samantha and Borycz, Jolanta A and Canino, Brandon S and Cheatham, Natasha and Cook, Michael and Dreher, Marisa and Duclos, Octave and Eubanks, Bryon and Fairbanks, Kelli and Finley, Samantha and Forknall, Nora and Francis, Audrey and Hopkins, Gary Patrick and Joyce, Emily M and Kim, SungJin and Kirk, Nicole A and Kovalyak, Julie and Lauchie, Shirley A and Lohff, Alanna and Maldonado, Charli and Manley, Emily A and McLin, Sari and Mooney, Caroline and Ndama, Miatta and Ogundeyi, Omotara and Okeoma, Nneoma and Ordish, Christopher and Padilla, Nicholas and Patrick, Christopher M and Paterson, Tyler and Phillips, Elliott E and Phillips, Emily M and Rampally, Neha and Ribeiro, Caitlin and Robertson, Madelaine K and Rymer, Jon Thomson and Ryan, Sean M and Sammons, Megan and Scott, Anne K and Scott, Ashley L and Shinomiya, Aya and Smith, Claire and Smith, Kelsey and Smith, Natalie L and Sobeski, Margaret A and Suleiman, Alia and Swift, Jackie and Takemura, Satoko and Talebi, Iris and Tarnogorska, Dorota and Tenshaw, Emily and Tokhi, Temour and Walsh, John J and Yang, Tansy and Horne, Jane Anne and Li, Feng and Parekh, Ruchi and Rivlin, Patricia K and Jayaraman, Vivek and Costa, Marta and Jefferis, Gregory Sxe and Ito, Kei and Saalfeld, Stephan and George, Reed and Meinertzhagen, Ian A and Rubin, Gerald M and Hess, Harald F and Jain, Viren and Plaza, Stephen M} } @article {65910, title = {An unbiased template of the Drosophila brain and ventral nerve cord.}, journal = {PLoS One}, volume = {15}, year = {2020}, month = {2020 Dec 31}, pages = {e0236495}, abstract = {

The fruit fly Drosophila melanogaster is an important model organism for neuroscience with a wide array of genetic tools that enable the mapping of individual neurons and neural subtypes. Brain templates are essential for comparative biological studies because they enable analyzing many individuals in a common reference space. Several central brain templates exist for Drosophila, but every one is either biased, uses sub-optimal tissue preparation, is imaged at low resolution, or does not account for artifacts. No publicly available Drosophila ventral nerve cord template currently exists. In this work, we created high-resolution templates of the Drosophila brain and ventral nerve cord using the best-available technologies for imaging, artifact correction, stitching, and template construction using groupwise registration. We evaluated our central brain template against the four most competitive, publicly available brain templates and demonstrate that ours enables more accurate registration with fewer local deformations in shorter time.

}, issn = {1932-6203}, doi = {10.1371/journal.pone.0236495}, author = {Bogovic, John A and Otsuna, Hideo and Heinrich, Larissa and Ito, Masayoshi and Jeter, Jennifer and Meissner, Geoffrey and Nern, Aljoscha and Colonell, Jennifer and Malkesman, Oz and Ito, Kei and Saalfeld, Stephan} } @inbook {65065, title = {Computational methods for stitching, alignment, and artifact correction of serial section data.}, booktitle = {Methods in Cell Biology}, volume = {152}, year = {2019}, pages = {261 - 276}, publisher = {Elsevier}, organization = {Elsevier}, abstract = {

Imaging large samples at the resolution offered by\ electron microscopy\ is typically achieved by sequentially recording overlapping tiles that are later combined to seamless\ mosaics. Mosaics of serial sections are aligned to reconstruct three-dimensional volumes. To achieve this, image distortions and artifacts as introduced during sample preparation or imaging need to be removed.

In this chapter, we will discuss typical sources of artifacts and distortion, and we will learn how to use the open source software TrakEM2 to correct them.

}, isbn = {9780128170182}, issn = {0091679X}, doi = {10.1016/bs.mcb.2019.04.007}, url = {https://doi.org/10.1016/bs.mcb.2019.04.007}, author = {Saalfeld, Stephan} } @article {48806, title = {Large scale image segmentation with structured loss based deep learning for connectome reconstruction.}, journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence}, volume = {41}, year = {2019}, month = {2019 Jul 1}, pages = {1669-80}, abstract = {

We present a method combining affinity prediction with region agglomeration, which improves significantly upon the state of the art of neuron segmentation from electron microscopy (EM) in accuracy and scalability. Our method consists of a 3D U-net, trained to predict affinities between voxels, followed by iterative region agglomeration. We train using a structured loss based on MALIS, encouraging topologically correct segmentations obtained from affinity thresholding. Our extension consists of two parts: First, we present a quasi-linear method to compute the loss gradient, improving over the original quadratic algorithm. Second, we compute the gradient in two separate passes to avoid spurious gradient contributions in early training stages. Our predictions are accurate enough that simple learning-free percentile-based agglomeration outperforms more involved methods used earlier on inferior predictions. We present results on three diverse EM datasets, achieving relative improvements over previous results of 27\%, 15\%, and 250\%. Our findings suggest that a single method can be applied to both nearly isotropic block-face EM data and anisotropic serial sectioned EM data. The runtime of our method scales linearly with the size of the volume and achieves a throughput of ~2.6 seconds per megavoxel, qualifying our method for the processing of very large datasets.

}, doi = {10.1109/TPAMI.2018.2835450}, author = {Funke, Jan and Tschopp, Fabian D and Grisaitis, William and Sheridan, Arlo and Singh, Chandan and Saalfeld, Stephan and Turaga, Srinivas C} } @article {49177, title = {Joint deformable registration of large EM image volumes: a matrix solver approach.}, journal = {arXiv}, year = {2018}, month = {2018 Apr 26}, abstract = {

Large electron microscopy image datasets for connectomics are typically composed of thousands to millions of partially overlapping two-dimensional images (tiles), which must be registered into a coherent volume prior to further analysis. A common registration strategy is to find matching features between neighboring and overlapping image pairs, followed by a numerical estimation of optimal image deformation using a so-called solver program.\ 
Existing solvers are inadequate for large data volumes, and inefficient for small-scale image registration.\ 
In this work, an efficient and accurate matrix-based solver method is presented. A linear system is constructed that combines minimization of feature-pair square distances with explicit constraints in a regularization term. In absence of reliable priors for regularization, we show how to construct a rigid-model approximation to use as prior. The linear system is solved using available computer programs, whose performance on typical registration tasks we briefly compare, and to which future scale-up is delegated. Our method is applied to the joint alignment of 2.67 million images, with more than 200 million point-pairs and has been used for successfully aligning the first full adult fruit fly brain.

}, url = {https://arxiv.org/abs/1804.10019}, author = {Khairy, Khaled and Denisov, Gennady and Saalfeld, Stephan} } @article {49220, title = {Synaptic cleft segmentation in non-isotropic volume electron microscopy of the complete Drosophila brain.}, journal = {Medical Image Computing and Computer Assisted Intervention {\textendash} MICCAI 2018}, year = {2018}, month = {2018 Sep 26}, pages = {317-25}, abstract = {

Neural circuit reconstruction at single synapse resolution is increasingly recognized as crucially important to decipher the function of biological nervous systems. Volume electron microscopy in serial transmission or scanning mode has been demonstrated to provide the necessary resolution to segment or trace all neurites and to annotate all synaptic connections.\ 
Automatic annotation of synaptic connections has been done successfully in near isotropic electron microscopy of vertebrate model organisms. Results on non-isotropic data in insect models, however, are not yet on par with human annotation.\ 
We designed a new 3D-U-Net architecture to optimally represent isotropic fields of view in non-isotropic data. We used regression on a signed distance transform of manually annotated synaptic clefts of the CREMI challenge dataset to train this model and observed significant improvement over the state of the art.\ 
We developed open source software for optimized parallel prediction on very large volumetric datasets and applied our model to predict synaptic clefts in a 50 tera-voxels dataset of the complete Drosophila brain. Our model generalizes well to areas far away from where training data was available.

}, doi = {10.1007/978-3-030-00934-2_36}, author = {Heinrich, Larissa and Funke, Jan and Pape, Constantin and Nunez-Iglesias, Juan and Saalfeld, Stephan} } @article {49386, title = {An unbiased template of the Drosophila brain and ventral nerve cord.}, journal = {bioRxiv}, year = {2018}, month = {2018 Jul 25}, abstract = {

The fruit fly Drosophila melanogaster is an important model organism for neuroscience with a wide array of genetic tools that enable the mapping of individuals neurons and neural subtypes. Brain templates are essential for comparative biological studies because they enable analyzing many individuals in a common reference space. Several central brain templates exist for Drosophila, but every one is either biased, uses sub-optimal tissue preparation, is imaged at low resolution, or does not account for artifacts. No publicly available Drosophila ventral nerve cord template currently exists. In this work, we created high-resolution templates of the Drosophila brain and ventral nerve cord using the best-available technologies for imaging, artifact correction, stitching, and template construction using groupwise registration. We evaluated our central brain template against the four most competitive, publicly available brain templates and demonstrate that ours enables more accurate registration with fewer local deformations in shorter time.

}, doi = {10.1101/376384}, author = {Bogovic, John A and Otsuna, Hideo and Heinrich, Larissa and Ito, Masayoshi and Jeter, Jennifer and Meissner, Geoffrey W and Nern, Aljoscha and Colonell, Jennifer and Malkesman, Oz and Saalfeld, Stephan} } @article {48645, title = {Building bridges between cellular and molecular structural biology.}, journal = {eLife}, volume = {6}, year = {2017}, month = {2017 Jul 06}, abstract = {

The integration of cellular and molecular structural data is key to understanding the function of macromolecular assemblies and complexes in their in vivo context. Here we report on the outcomes of a workshop that discussed how to integrate structural data from a range of public archives. The workshop identified two main priorities: the development of tools and file formats to support segmentation (that is, the decomposition of a three-dimensional volume into regions that can be associated with defined objects), and the development of tools to support the annotation of biological structures.

}, issn = {2050-084X}, doi = {10.7554/eLife.25835}, author = {Patwardhan, Ardan and Brandt, Robert and Butcher, Sarah J and Collinson, Lucy and Gault, David and Gr{\"u}newald, Kay and Hecksel, Corey and Huiskonen, Juha T and Iudin, Andrii and Jones, Martin L and Korir, Paul K and Koster, Abraham J and Lagerstedt, Ingvar and Lawson, Catherine L and Mastronarde, David and McCormick, Matthew and Parkinson, Helen and Rosenthal, Peter B and Saalfeld, Stephan and Saibil, Helen R and Sarntivijai, Sirarat and Solanes Valero, Irene and Subramaniam, Sriram and Swedlow, Jason R and Tudose, Ilinca and Winn, Martyn and Kleywegt, Gerard J} } @article {48647, title = {The candidate multi-cut for cell segmentation.}, journal = {2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018)}, year = {2017}, month = {2017 Jul 04}, abstract = {

Two successful approaches for the segmentation of biomedical images are (1) the selection of segment candidates from a merge-tree, and (2) the clustering of small superpixels by solving a Multi-Cut problem. In this paper, we introduce a model that unifies both approaches. Our model, the Candidate Multi-Cut (CMC), allows joint selection and clustering of segment candidates from a merge-tree. This way, we overcome the respective limitations of the individual methods: (1) the space of possible segmentations is not constrained to candidates of a merge-tree, and (2) the decision for clustering can be made on candidates larger than superpixels, using features over larger contexts. We solve the optimization problem of selecting and clustering of candidates using an integer linear program. On datasets of 2D light microscopy of cell populations and 3D electron microscopy of neurons, we show that our method generalizes well and generates more accurate segmentations than merge-tree or Multi-Cut methods alone.

}, doi = {10.1109/ISBI.2018.8363658}, author = {Funke, Jan and Zhang, Chong and Pietzsch, Tobias and Gonzalez Ballester, Miguel A and Saalfeld, Stephan} } @article {48602, title = {Deep learning for isotropic super-resolution from non-isotropic 3D electron microscopy.}, journal = {International Conference on Medical Image Computing and Computer-Assisted Intervention - MICCAI 2017. MICCAI 2017. Lecture Notes in Computer Science}, volume = {10434}, year = {2017}, month = {2017 Sept 4}, pages = {arXiv:1706.03142}, abstract = {

The most sophisticated existing methods to generate 3D isotropic super-resolution (SR) from non-isotropic electron microscopy (EM) are based on learned dictionaries. Unfortunately, none of the existing methods generate practically satisfying results. For 2D natural images, recently developed super-resolution methods that use deep learning have been shown to significantly outperform the previous state of the art. We have adapted one of the most successful architectures (FSRCNN) for 3D super-resolution, and compared its performance to a 3D U-Net architecture that has not been used previously to generate super-resolution. We trained both architectures on artificially downscaled isotropic ground truth from focused ion beam milling scanning EM (FIB-SEM) and tested the performance for various hyperparameter settings.

Our results indicate that both architectures can successfully generate 3D isotropic super-resolution from non-isotropic EM, with the U-Net performing consistently better. We propose several promising directions for practical application.

}, doi = {10.1007/978-3-319-66185-8_16}, author = {Heinrich, Larissa and Bogovic, John A and Saalfeld, Stephan} } @article {48418, title = {PreMosa: Extracting 2D surfaces from 3D microscopy mosaics.}, journal = {Bioinformatics (Oxford, England)}, volume = {33}, year = {2017}, month = {2017 Apr 05}, pages = {2563-9}, abstract = {

Motivation: A significant focus of biological research is to understand the development, organization and function of tissues. A particularly productive area of study is on single layer epithelial tissues in which the adherence junctions of cells form a 2D manifold that is fluorescently labeled. Given the size of the tissue, a microscope must collect a mosaic of overlapping 3D stacks encompassing the stained surface. Downstream interpretation is greatly simplified by preprocessing such a dataset as follows: (a) extracting and mapping the stained manifold in each stack into a single 2D projection plane, (b) correcting uneven illumination artifacts, (c) stitching the mosaic planes into a single, large 2D image, and (d) adjusting the contrast.

Results: We have developed PreMosa, an efficient, fully automatic pipeline to perform the four preprocessing tasks above resulting in a single 2D image of the stained manifold across which contrast is optimized and illumination is even. Notable features are as follows. First, the 2D projection step employs a specially developed algorithm that actually finds the manifold in the stack based on maximizing contrast, intensity and smoothness. Second, the projection step comes first, implying all subsequent tasks are more rapidly solved in 2D. And last, the mosaic melding employs an algorithm that globally adjusts contrasts amongst the 2D tiles so as to produce a seamless, high-contrast image. We conclude with an evaluation using ground-truth datasets and present results on datasets from Drosophila melanogaster wings and Schmidtae mediterranea ciliary components.

Availability: PreMosa is available under https://cblasse.github.io/premosa.

Contact: blasse@mpi-cbg.de, myers@mpi-cbg.de.

}, issn = {1367-4811}, doi = {10.1093/bioinformatics/btx195}, author = {Blasse, Corinna and Saalfeld, Stephan and Etournay, Rapha{\"e}l and Sagner, Andreas and Eaton, Suzanne and Myers, Eugene W} } @article {48497, title = {Whole-brain serial-section electron microscopy in larval zebrafish.}, journal = {Nature}, volume = {545}, year = {2017}, month = {2017 May 18}, pages = {345-349}, abstract = {

High-resolution serial-section electron microscopy (ssEM) makes it possible to investigate the dense meshwork of axons, dendrites, and synapses that form neuronal circuits. However, the imaging scale required to comprehensively reconstruct these structures is more than ten orders of magnitude smaller than the spatial extents occupied by networks of interconnected neurons, some of which span nearly the entire brain. Difficulties in generating and handling data for large volumes at nanoscale resolution have thus restricted vertebrate studies to fragments of circuits. These efforts were recently transformed by advances in computing, sample handling, and imaging techniques, but high-resolution examination of entire brains remains a challenge. Here, we present ssEM data for the complete brain of a larval zebrafish (Danio rerio) at 5.5 days post-fertilization. Our approach utilizes multiple rounds of targeted imaging at different scales to reduce acquisition time and data management requirements. The resulting dataset can be analysed to reconstruct neuronal processes, permitting us to survey all myelinated axons (the projectome). These reconstructions enable precise investigations of neuronal morphology, which reveal remarkable bilateral symmetry in myelinated reticulospinal and lateral line afferent axons. We further set the stage for whole-brain structure-function comparisons by co-registering functional reference atlases and in vivo two-photon fluorescence microscopy data from the same specimen. All obtained images and reconstructions are provided as an open-access resource.

}, issn = {1476-4687}, doi = {10.1038/nature22356}, author = {Hildebrand, David Grant Colburn and Cicconet, Marcelo and Torres, Russel Miguel and Choi, Woohyuk and Quan, Tran Minh and Moon, Jungmin and Wetzel, Arthur Willis and Scott Champion, Andrew and Graham, Brett Jesse and Randlett, Owen and Plummer, George Scott and Portugues, Ruben and Bianco, Isaac Henry and Saalfeld, Stephan and Baden, Alexander David and Lillaney, Kunal and Burns, Randal and Vogelstein, Joshua Tzvi and Schier, Alexander Franz and Lee, Wei-Chung Allen and Jeong, Won-Ki and Lichtman, Jeff William and Engert, Florian} } @article {48296, title = {Image-based correction of continuous and discontinuous non-planar axial distortion in serial section microscopy.}, journal = {Bioinformatics (Oxford, England)}, year = {2016}, month = {2016 Dec 23}, abstract = {

MOTIVATION: Serial section microscopy is an established method for detailed anatomy reconstruction of biological specimen. During the last decade, high resolution electron microscopy (EM) of serial sections has become the de-facto standard for reconstruction of neural connectivity at ever increasing scales (EM connectomics). In serial section microscopy, the axial dimension of the volume is sampled by physically removing thin sections from the embedded specimen and subsequently imaging either the block-face or the section series. This process has limited precision leading to inhomogeneous non-planar sampling of the axial dimension of the volume which, in turn, results in distorted image volumes. This includes that section series may be collected and imaged in unknown order.

RESULTS: We developed methods to identify and correct these distortions through image-based signal analysis without any additional physical apparatus or measurements. We demonstrate the efficacy of our methods in proof of principle experiments and application to real world problems.

AVAILABILITY AND IMPLEMENTATION: We made our work available as libraries for the ImageJ distribution Fiji and for deployment in a high performance parallel computing environment. Our sources are open and available at http://github.com/saalfeldlab/section-sort, http://github.com/saalfeldlab/z-spacing and http://github.com/saalfeldlab/z-spacing-spark CONTACT: : saalfelds@janelia.hhmi.orgSupplementary information: Supplementary data are available at Bioinformatics online.

}, issn = {1367-4811}, doi = {10.1093/bioinformatics/btw794}, author = {Hanslovsky, Philipp and Bogovic, John A and Saalfeld, Stephan} } @article {47062, title = {Quantitative neuroanatomy for connectomics in Drosophila.}, journal = {eLife}, year = {2016}, month = {2016 Mar 18}, pages = {e12059}, abstract = {

Neuronal circuit mapping using electron microscopy demands laborious proofreading or reconciliation of multiple independent reconstructions. Here, we describe new methods to apply quantitative arbor and network context to iteratively proofread and reconstruct circuits and create anatomically enriched wiring diagrams. We measured the morphological underpinnings of connectivity in new and existing reconstructions of\ Drosophila\ sensorimotor (larva) and visual (adult) systems. Synaptic inputs were preferentially located on numerous small, microtubule-free {\textquoteright}twigs{\textquoteright} which branch off a single microtubule-containing {\textquoteright}backbone{\textquoteright}. Omission of individual twigs accounted for 96\% of errors. However, the synapses of highly connected neurons were distributed across multiple twigs. Thus, the robustness of a strong connection to detailed twig anatomy was associated with robustness to reconstruction error. By comparing iterative reconstruction to the consensus of multiple reconstructions, we show that our method overcomes the need for redundant effort through the discovery and application of relationships between cellular neuroanatomy and synaptic connectivity.

}, doi = {10.7554/eLife.12059}, author = {Schneider-Mizell, Casey M. and Gerhard, Stephan and Longair, Mark and Kazimiers, Tom and Li, Feng and Zwart, Maarten and Champion, Andrew and Midgley, Frank and Fetter, Richard D and Saalfeld, Stephan and Cardona, Albert} } @conference {47735, title = {Robust registration of calcium images by learned contrast synthesis.}, booktitle = {IEEE 13th International Symposium on Biomedical Imaging: From Nano to Macro}, year = {2016}, month = {2016 Jun 15}, address = {Clarion Congress Hotel in Prague Prague; Czech Republic}, abstract = {

Multi-modal image registration is a challenging task that is vital to fuse complementary signals for subsequent analyses. Despite much research into cost functions addressing this challenge, there exist cases in which these are ineffective. In this work, we show that (1) this is true for the registration of in-vivo Drosophila brain volumes visualizing genetically encoded calcium indicators to an nc82 atlas and (2) that machine learning based contrast synthesis can yield improvements. More specifically, the number of subjects for which the registration outright failed was greatly reduced (from 40\% to 15\%) by using a synthesized image.

}, isbn = {978-147992350-2}, doi = {10.1109/ISBI.2016.7493463}, author = {Bogovic, John A and Hanslovsky, Philipp and Wong, Allen M and Saalfeld, Stephan} } @article {46817, title = {BigDataViewer: visualization and processing for large image data sets.}, journal = {Nature Methods}, volume = {12}, year = {2015}, month = {2015 May 28}, pages = {481-3}, issn = {1548-7105}, doi = {10.1038/nmeth.3392}, author = {Pietzsch, Tobias and Saalfeld, Stephan and Preibisch, Stephan and Tomancak, Pavel} } @article {46788, title = {Post-acquisition image based compensation for thickness variation in microscopy section series.}, journal = {IEEE International Symposium on Biomedical Imaging}, year = {2015}, month = {2014 Nov 25}, pages = {507-11}, abstract = {

Serial section Microscopy is an established method for volumetric anatomy reconstruction. Section series imaged with Electron Microscopy are currently vital for the reconstruction of the synaptic connectivity of entire animal brains such as that of Drosophila melanogaster. The process of removing ultrathin layers from a solid block containing the specimen, however, is a fragile procedure and has limited precision with respect to section thickness. We have developed a method to estimate the relative z-position of each individual section as a function of signal change across the section series. First experiments show promising results on both serial section Transmission Electron Microscopy (ssTEM) data and Focused Ion Beam Scanning Electron Microscopy (FIB-SEM) series. We made our solution available as Open Source plugins for the TrakEM2 software and the ImageJ distribution Fiji.

}, url = {http://arxiv.org/abs/1411.6970}, author = {Hanslovsky, Philpp and Bogovic, John A and Saalfeld, Stephan} } @conference {47129, title = {Post-acquisition image based compensation for thickness variation in microscopy section series.}, booktitle = {IEEE 12th International Symposium on Biomedical Imaging (ISBI)}, year = {2015}, month = {2015 Jul 01}, abstract = {

Serial section Microscopy is an established method for volumetric anatomy reconstruction. Section series imaged with Electron Microscopy are currently vital for the reconstruction of the synaptic connectivity of entire animal brains such as that of Drosophila melanogaster. The process of removing ultrathin layers from a solid block containing the specimen, however, is a fragile procedure and has limited precision with respect to section thickness. We have developed a method to estimate the relative z-position of each individual section as a function of signal change across the section series. First experiments show promising results on both serial section Transmission Electron Microscopy (ssTEM) data and Focused Ion Beam Scanning Electron Microscopy (FIB-SEM) series. We made our solution available as Open Source plugins for the TrakEM2 software and the ImageJ distribution Fiji.

}, doi = {10.1109/ISBI.2015.7163922}, author = {Hanslovsky, Philipp and Bogovic, John and Saalfeld, Stephan} } @article {46787, title = {Systematic imaging reveals features and changing localization of mRNAs in Drosophila development.}, journal = {Elife}, volume = {4}, year = {2015}, month = {2015}, abstract = {

mRNA localization is critical for eukaryotic cells and affects numerous transcripts, yet how cells regulate distribution of many mRNAs to their subcellular destinations is still unknown. We combined transcriptomics and systematic imaging to determine the tissue-specific expression and subcellular distribution of 5862 mRNAs during Drosophila oogenesis. mRNA localization is widespread in the ovary and detectable in all of its cell types-the somatic epithelial, the nurse cells, and the oocyte. Genes defined by a common RNA localization share distinct gene features and differ in expression level, 3{\textquoteright}UTR length and sequence conservation from unlocalized mRNAs. Comparison of mRNA localizations in different contexts revealed that localization of individual mRNAs changes over time in the oocyte and between ovarian and embryonic cell types. This genome scale image-based resource (Dresden Ovary Table, DOT, http://tomancak-srv1.mpi-cbg.de/DOT/main.html) enables the transition from mechanistic dissection of singular mRNA localization events towards global understanding of how mRNAs transcribed in the nucleus distribute in cells.

}, issn = {2050-084X}, doi = {10.7554/eLife.05003}, author = {Jambor, Helena and Surendranath, Vineeth and Kalinka, Alex T and Mejstrik, Pavel and Saalfeld, Stephan and Tomancak, Pavel} } @proceedings {46790, title = {Advanced Programming with ImgLib2}, year = {2012}, month = {2012 Oct 24}, address = {Mondorf-les-Bains, Luxembourg}, author = {Pietzsch, Tobias and Preibisch, Stephan and Tomancak, Pavel and Saalfeld, Stephan} } @article {5681, title = {Elastic volume reconstruction from series of ultra-thin microscopy sections.}, journal = {Nature Methods}, volume = {9}, year = {2012}, month = {2012 Jul}, pages = {717-20}, abstract = {

Anatomy of large biological specimens is often reconstructed from serially sectioned volumes imaged by high-resolution microscopy. We developed a method to reassemble a continuous volume from such large section series that explicitly minimizes artificial deformation by applying a global elastic constraint. We demonstrate our method on a series of transmission electron microscopy sections covering the entire 558-cell Caenorhabditis elegans embryo and a segment of the Drosophila melanogaster larval ventral nerve cord.

}, keywords = {Animals, Automated, Caenorhabditis elegans, Computer-Assisted, Drosophila melanogaster, Elasticity, Electron, Embryo, Image Processing, Larva, Microscopy, Microtomy, Nonmammalian, Pattern Recognition, Transmission}, issn = {1548-7105}, doi = {10.1038/nmeth.2072}, author = {Saalfeld, Stephan and Fetter, Richard D and Cardona, Albert and Tomancak, Pavel.} } @article {5634, title = {Fiji: an open-source platform for biological-image analysis.}, journal = {Nature Methods}, volume = {9}, year = {2012}, month = {2012 Jul}, pages = {676-82}, abstract = {

Fiji is a distribution of the popular open-source software ImageJ focused on biological-image analysis. Fiji uses modern software engineering practices to combine powerful software libraries with a broad range of scripting languages to enable rapid prototyping of image-processing algorithms. Fiji facilitates the transformation of new algorithms into ImageJ plugins that can be shared with end users through an integrated update system. We propose Fiji as a platform for productive collaboration between computer science and biology research communities.

}, keywords = {Algorithms, Animals, Brain, Computational Biology, Computer-Assisted, Drosophila melanogaster, Image Enhancement, Image Processing, Imaging, Information Dissemination, Software, Software Design, Three-Dimensional}, issn = {1548-7105}, doi = {10.1038/nmeth.2019}, author = {Schindelin, Johannes and Arganda-Carreras, Ignacio and Frise, Erwin and Kaynig, Verena and Longair, Mark and Pietzsch, Tobias and Preibisch, Stephan and Rueden, Curtis and Saalfeld, Stephan and Schmid, Benjamin and Tinevez, Jean-Yves and White, Daniel James and Hartenstein, Volker and Eliceiri, Kevin and Tomancak, Pavel and Cardona, Albert} } @article {46160, title = {ImgLib2--generic image processing in Java.}, journal = {Bioinformatics}, volume = {28}, year = {2012}, month = {2012 Nov 15}, pages = {3009-11}, abstract = {

SUMMARY: ImgLib2 is an open-source Java library for n-dimensional data representation and manipulation with focus on image processing. It aims at minimizing code duplication by cleanly separating pixel-algebra, data access and data representation in memory. Algorithms can be implemented for classes of pixel types and generic access patterns by which they become independent of the specific dimensionality, pixel type and data representation. ImgLib2 illustrates that an elegant high-level programming interface can be achieved without sacrificing performance. It provides efficient implementations of common data types, storage layouts and algorithms. It is the data model underlying ImageJ2, the KNIME Image Processing toolbox and an increasing number of Fiji-Plugins.

AVAILABILITY: ImgLib2 is licensed under BSD. Documentation and source code are available at http://imglib2.net and in a public repository at https://github.com/imagej/imglib.

SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics Online.

CONTACT: saalfeld@mpi-cbg.de

}, keywords = {Algorithms, Image Processing, Computer-Assisted, Programming Languages, Software}, issn = {1367-4811}, doi = {10.1093/bioinformatics/bts543}, author = {Pietzsch, Tobias and Preibisch, Stephan and Tomancak, Pavel and Saalfeld, Stephan} } @proceedings {46789, title = {ImgLib2{\textemdash}Generic Image Processing in Java}, year = {2012}, month = {2012 Oct 24}, address = {Mondorf-les-Bains, Luxembourg}, author = {Saalfeld, Stephan and Pietzsch, Tobias and Tomancak, Pavel and Preibisch, Stephan} } @proceedings {46791, title = {Introduction to ImgLib2}, year = {2012}, month = {2012 Oct 24}, address = {Mondorf-les-Bains, Luxembourg}, author = {Preibisch, Stephan and Pietzsch, Tobias and Myers, Eugene and Tomancak, Pavel and Saalfeld, Stephan} } @article {5684, title = {TrakEM2 software for neural circuit reconstruction.}, journal = {PLoS One}, volume = {7}, year = {2012}, month = {2012}, pages = {e38011}, abstract = {

A key challenge in neuroscience is the expeditious reconstruction of neuronal circuits. For model systems such as Drosophila and C. elegans, the limiting step is no longer the acquisition of imagery but the extraction of the circuit from images. For this purpose, we designed a software application, TrakEM2, that addresses the systematic reconstruction of neuronal circuits from large electron microscopical and optical image volumes. We address the challenges of image volume composition from individual, deformed images; of the reconstruction of neuronal arbors and annotation of synapses with fast manual and semi-automatic methods; and the management of large collections of both images and annotations. The output is a neural circuit of 3d arbors and synapses, encoded in NeuroML and other formats, ready for analysis.

}, keywords = {Animals, Models, Nerve Net, Neuroimaging, Neurological, Neurons, Software, Synapses, Synaptic Transmission}, issn = {1932-6203}, doi = {10.1371/journal.pone.0038011}, author = {Cardona, Albert and Saalfeld, Stephan and Schindelin, Johannes and Arganda-Carreras, Ignacio and Preibisch, Stephan and Longair, Mark and Tomancak, Pavel and Hartenstein, Volker and Douglas, Rodney J} } @article {46785, title = {TrakEM2 software for neural circuit reconstruction.}, journal = {PLoS One}, volume = {7}, year = {2012}, month = {2012}, pages = {e38011}, abstract = {

A key challenge in neuroscience is the expeditious reconstruction of neuronal circuits. For model systems such as Drosophila and C. elegans, the limiting step is no longer the acquisition of imagery but the extraction of the circuit from images. For this purpose, we designed a software application, TrakEM2, that addresses the systematic reconstruction of neuronal circuits from large electron microscopical and optical image volumes. We address the challenges of image volume composition from individual, deformed images; of the reconstruction of neuronal arbors and annotation of synapses with fast manual and semi-automatic methods; and the management of large collections of both images and annotations. The output is a neural circuit of 3d arbors and synapses, encoded in NeuroML and other formats, ready for analysis.

}, keywords = {Animals, Models, Neurological, Nerve Net, Neuroimaging, Neurons, Software, Synapses, Synaptic Transmission}, issn = {1932-6203}, doi = {10.1371/journal.pone.0038011}, author = {Cardona, Albert and Saalfeld, Stephan and Schindelin, Johannes and Arganda-Carreras, Ignacio and Preibisch, Stephan and Longair, Mark and Tomancak, Pavel and Hartenstein, Volker and Douglas, Rodney J} } @article {5690, title = {As-rigid-as-possible mosaicking and serial section registration of large ssTEM datasets.}, journal = {Bioinformatics}, volume = {26}, year = {2010}, month = {2010 Jun 15}, pages = {i57-63}, abstract = {

Tiled serial section Transmission Electron Microscopy (ssTEM) is increasingly used to describe high-resolution anatomy of large biological specimens. In particular in neurobiology, TEM is indispensable for analysis of synaptic connectivity in the brain. Registration of ssTEM image mosaics has to recover the 3D continuity and geometrical properties of the specimen in presence of various distortions that are applied to the tissue during sectioning, staining and imaging. These include staining artifacts, mechanical deformation, missing sections and the fact that structures may appear dissimilar in consecutive sections.

}, keywords = {Animals, Databases, Drosophila, Electron, Factual, Imaging, Microscopy, Three-Dimensional, Transmission}, issn = {1367-4811}, doi = {10.1093/bioinformatics/btq219}, author = {Saalfeld, Stephan and Cardona, Albert and Hartenstein, Volker and Tomancak, Pavel.} } @article {5689, title = {Identifying neuronal lineages of Drosophila by sequence analysis of axon tracts.}, journal = {The Journal of Neuroscience}, volume = {30}, year = {2010}, month = {2010 Jun 2}, pages = {7538-53}, abstract = {

The Drosophila brain is formed by an invariant set of lineages, each of which is derived from a unique neural stem cell (neuroblast) and forms a genetic and structural unit of the brain. The task of reconstructing brain circuitry at the level of individual neurons can be made significantly easier by assigning neurons to their respective lineages. In this article we address the automation of neuron and lineage identification. We focused on the Drosophila brain lineages at the larval stage when they form easily recognizable secondary axon tracts (SATs) that were previously partially characterized. We now generated an annotated digital database containing all lineage tracts reconstructed from five registered wild-type brains, at higher resolution and including some that were previously not characterized. We developed a method for SAT structural comparisons based on a dynamic programming approach akin to nucleotide sequence alignment and a machine learning classifier trained on the annotated database of reference SATs. We quantified the stereotypy of SATs by measuring the residual variability of aligned wild-type SATs. Next, we used our method for the identification of SATs within wild-type larval brains, and found it highly accurate (93-99\%). The method proved highly robust for the identification of lineages in mutant brains and in brains that differed in developmental time or labeling. We describe for the first time an algorithm that quantifies neuronal projection stereotypy in the Drosophila brain and use the algorithm for automatic neuron and lineage recognition.

}, keywords = {Algorithms, Animals, Automatic Data Processing, Axons, Brain, Brain Mapping, Caspase 3, Cell Lineage, Computer-Assisted, Confocal, Dendrites, Drosophila, Drosophila Proteins, Embryo, Genetically Modified, Green Fluorescent Proteins, Image Processing, Microscopy, Models, Neurological, Neurons, Nonmammalian, Reproducibility of Results, Software}, issn = {1529-2401}, doi = {10.1523/JNEUROSCI.0186-10.2010}, author = {Cardona, Albert and Saalfeld, Stephan and Arganda, Ignacio and Pereanu, Wayne and Schindelin, Johannes and Hartenstein, Volker.} } @article {5687, title = {An integrated micro- and macroarchitectural analysis of the Drosophila brain by computer-assisted serial section electron microscopy.}, journal = {PLoS Biology}, volume = {8}, year = {2010}, month = {2010}, abstract = {

The analysis of microcircuitry (the connectivity at the level of individual neuronal processes and synapses), which is indispensable for our understanding of brain function, is based on serial transmission electron microscopy (TEM) or one of its modern variants. Due to technical limitations, most previous studies that used serial TEM recorded relatively small stacks of individual neurons. As a result, our knowledge of microcircuitry in any nervous system is very limited. We applied the software package TrakEM2 to reconstruct neuronal microcircuitry from TEM sections of a small brain, the early larval brain of Drosophila melanogaster. TrakEM2 enables us to embed the analysis of the TEM image volumes at the microcircuit level into a light microscopically derived neuro-anatomical framework, by registering confocal stacks containing sparsely labeled neural structures with the TEM image volume. We imaged two sets of serial TEM sections of the Drosophila first instar larval brain neuropile and one ventral nerve cord segment, and here report our first results pertaining to Drosophila brain microcircuitry. Terminal neurites fall into a small number of generic classes termed globular, varicose, axiform, and dendritiform. Globular and varicose neurites have large diameter segments that carry almost exclusively presynaptic sites. Dendritiform neurites are thin, highly branched processes that are almost exclusively postsynaptic. Due to the high branching density of dendritiform fibers and the fact that synapses are polyadic, neurites are highly interconnected even within small neuropile volumes. We describe the network motifs most frequently encountered in the Drosophila neuropile. Our study introduces an approach towards a comprehensive anatomical reconstruction of neuronal microcircuitry and delivers microcircuitry comparisons between vertebrate and insect neuropile.

}, keywords = {Animals, Brain, Computer-Assisted, Drosophila melanogaster, Electron, Humans, Image Processing, Imaging, Microscopy, Neurons, Neuropil, Software, Synapses, Three-Dimensional, Transmission}, issn = {1545-7885}, doi = {10.1371/journal.pbio.1000502}, author = {Cardona, Albert and Saalfeld, Stephan and Preibisch, Stephan and Schmid, Benjamin and Cheng, Anchi and Pulokas, Jim and Tomancak, Pavel and Hartenstein, Volker.} } @proceedings {46792, title = {Into ImgLib{\textemdash}Generic image processing in Java}, year = {2010}, month = {2012 Oct 27}, address = {Mondorf-les-Bains, Luxembourg}, abstract = {

The purpose of ImgLib, a Generic Java Image Processing Library, is to provide an abstract framework enabling Java developers to design and implement data processing algorithms without having to consider dimensionality, type of data (e. g. byte, float, complex float), or strategies for data access (e. g. linear arrays, cells, paged cells). This kind of programming has significant advantages over the classical way. An algorithm written once for a certain class of Type will potentially run on any compatible Type, even if it does not exist yet. Same applies for data access strategies and the number of dimensions.
We achieve this abstraction by accessing data through Iterators and Type interfaces. Iterators guarantee efficient traversal through pixels depending on whether random coordinate access is required or just all pixels have to be visited once, whether real or integer coordinates are accessed, whether coordinates outside of image boundaries are accessed or not. Type interfaces define the supported operators on pixel values (like basic algebra) and hide the underlying basic type from algorithm implementation.

}, author = {Preibisch, Stephan and Tomancak, Pavel and Saalfeld, Stephan} } @article {46161, title = {Software for bead-based registration of selective plane illumination microscopy data.}, journal = {Nature Methods}, volume = {7}, year = {2010}, month = {2010 Jun}, pages = {418-9}, keywords = {Animals, Caenorhabditis elegans, Drosophila melanogaster, Imaging, Three-Dimensional, Lighting, Microscopy, Software}, issn = {1548-7105}, doi = {10.1038/nmeth0610-418}, author = {Preibisch, Stephan and Saalfeld, Stephan and Schindelin, Johannes and Tomancak, Pavel} } @proceedings {46793, title = {Bead-based mosaicing of single plane illumination microscopy images using geometric local descriptor matching.}, volume = {7259}, year = {2009}, month = {2009 Mar 27}, pages = {72592S}, publisher = {SPIE}, address = {Lake Buena Vista, FL}, abstract = {

Single Plane Illumination Microscopy (SPIM) is an emerging microscopic technique that enables live imaging of large biological specimens in their entirety. By imaging the biological sample from multiple angles, SPIM has the potential to achieve isotropic resolution throughout relatively large biological specimens. For every angle, however, only a shallow section of the specimen is imaged with high resolution, whereas deeper regions appear increasingly blurred. Existing intensity-based registration techniques still struggle to robustly and accurately align images that are characterized by limited overlap and/or heavy blurring. To be able to register such images, we add sub-resolution fluorescent beads to the rigid agarose medium in which the imaged specimen is embedded. For each segmented bead, we store the relative location of its n nearest neighbors in image space as rotation-invariant geometric local descriptors. Corresponding beads between overlapping images are identified by matching these descriptors. The bead correspondences are used to simultaneously estimate the globally optimal transformation for each individual image. The final output image is created by combining all images in an angle-independent output space, using volume injection and local content-based weighting of contributing images. We demonstrate the performance of our approach on data acquired from living embryos of Drosophila and fixed adult C.elegans worms. Bead-based registration outperformed intensity-based registration in terms of computation speed by two orders of magnitude while producing bead registration errors below 1 μm (about 1 pixel). It, therefore, provides an ideal tool for processing of long term time-lapse recordings of embryonic development consisting of hundreds of time points.

}, doi = {10.1117/12.812612}, author = {Preibisch, Stephan and Saalfeld, Stephan and Rohlfing, Torsten and Tomancak, Pavel} } @article {5817, title = {CATMAID: collaborative annotation toolkit for massive amounts of image data.}, journal = {Bioinformatics}, volume = {25}, year = {2009}, month = {2009 Aug 1}, pages = {1984-6}, abstract = {

SUMMARY: High-resolution, three-dimensional (3D) imaging of large biological specimens generates massive image datasets that are difficult to navigate, annotate and share effectively. Inspired by online mapping applications like GoogleMaps, we developed a decentralized web interface that allows seamless navigation of arbitrarily large image stacks. Our interface provides means for online, collaborative annotation of the biological image data and seamless sharing of regions of interest by bookmarking. The CATMAID interface enables synchronized navigation through multiple registered datasets even at vastly different scales such as in comparisons between optical and electron microscopy. AVAILABILITY: http://fly.mpi-cbg.de/catmaid.

}, keywords = {Algorithms, Computational Biology, Computer Graphics, Database Management Systems, Databases, Factual, Imaging, Information Storage and Retrieval, Software, Three-Dimensional}, issn = {1367-4811}, doi = {10.1093/bioinformatics/btp266}, author = {Saalfeld, Stephan and Cardona, Albert and Hartenstein, Volker and Tomancak, Pavel} } @article {46786, title = {Globally optimal stitching of tiled 3D microscopic image acquisitions.}, journal = {Bioinformatics}, volume = {25}, year = {2009}, month = {2009 Jun 1}, pages = {1463-5}, abstract = {

MOTIVATION: Modern anatomical and developmental studies often require high-resolution imaging of large specimens in three dimensions (3D). Confocal microscopy produces high-resolution 3D images, but is limited by a relatively small field of view compared with the size of large biological specimens. Therefore, motorized stages that move the sample are used to create a tiled scan of the whole specimen. The physical coordinates provided by the microscope stage are not precise enough to allow direct reconstruction (Stitching) of the whole image from individual image stacks.

RESULTS: To optimally stitch a large collection of 3D confocal images, we developed a method that, based on the Fourier Shift Theorem, computes all possible translations between pairs of 3D images, yielding the best overlap in terms of the cross-correlation measure and subsequently finds the globally optimal configuration of the whole group of 3D images. This method avoids the propagation of errors by consecutive registration steps. Additionally, to compensate the brightness differences between tiles, we apply a smooth, non-linear intensity transition between the overlapping images. Our stitching approach is fast, works on 2D and 3D images, and for small image sets does not require prior knowledge about the tile configuration.

AVAILABILITY: The implementation of this method is available as an ImageJ plugin distributed as a part of the Fiji project (Fiji is just ImageJ: http://pacific.mpi-cbg.de/).

}, keywords = {Animals, Drosophila, Imaging, Three-Dimensional, Larva, Microscopy, Confocal}, issn = {1367-4811}, doi = {10.1093/bioinformatics/btp184}, author = {Preibisch, Stephan and Saalfeld, Stephan and Tomancak, Pavel} } @proceedings {46794, title = {Automatic landmark correspondence detection for ImageJ.}, year = {2008}, month = {2008 Nov 7}, address = {Luxembourg}, abstract = {

Landmark correspondences can be used for various tasks in image processing such as image alignment, reconstruction of panoramic photographs, object recognition and simultaneous localization and mapping for mobile robots. The computer vision community knows several techniques for extracting and pairwise associating such landmarks using distinctive invariant local image features. Two very successful methods are the Scale Invariant Feature Transform (SIFT)1 and Multi-Scale Oriented Patches (MOPS).2
We implemented these methods in the Java programming language3 for seamless use in ImageJ.4 We use it for fully automatic registration of gigantic serial section Transmission Electron Microscopy (TEM) mosaics. Using automatically detected landmark correspondences, the registration of large image mosaics simplifies to globally minimizing the displacement of corresponding points.
We present here an introduction to automatic landmark correspondence detection and demonstrate our implementation for ImageJ. We demonstrate the application of the plug-in on diverse image data.

}, author = {Saalfeld, Stephan and Tomancak, Pavel} } @proceedings {46795, title = {Fast stitching of large 3d biological datasets.}, year = {2008}, month = {2008 Nov 7}, address = {Luxembourg}, abstract = {

In order to study anatomy of organisms with high-resolution there is an increasing demand to image large specimen in three dimensions (3D). Confocal microscopy is able to produce high-resolution 3D images, but these are limited by its relatively small field of view compared to the size of large biological specimens. To overcome this drawback, motorized stages moving the sample are used to create a tiled scan of the whole specimen. The physical coordinates provided by the microscope stage are not precise enough to allow reconstruction ({\textquotedblright}Stitching{\textquotedblright}) of the whole image from individual image stacks.
We developed an algorithm, as well as an ImageJ plug-in, based on the Fourier Shift Theorem that computes all possible translations (x, y, z) between two 3D images at once, yielding the best overlap in terms of the cross correlation measure. Apart from the obvious gain in computation time it has the advantage that it cannot be trapped in local minima as it simply computes all possible solutions. Computing the overlap between two adjacent image stacks is fast (12 seconds for two 512x512x89 images on a Intel {\textregistered} Core2Duo with 2.2GHz) making it suitable for real time use, i.e. computing the output image during acquisition of the individual image stacks.
To compensate the possible shading- and brightness differences we apply a smooth linear intensity transition between the overlapping stacks. Additionally we extended the to generic 3D registration using gradient based rotation detection on top of the phase correlation method. We demonstrate the performance of our 3D stitching plug-in on several tiled confocal images and show an example of its application for 3D registration.

}, author = {Preibisch, Stephan and Saalfeld, Stephan and Tomancak, Pavel} }