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.

High-throughput electron microscopy allows recording of lar- ge stacks of neural tissue with sufficient resolution to extract the wiring diagram of the underlying neural network. Current efforts to automate this process focus mainly on the segmentation of neurons. However, in order to recover a wiring diagram, synaptic partners need to be identi- fied as well. This is especially challenging in insect brains like Drosophila melanogaster, where one presynaptic site is associated with multiple post- synaptic elements. Here we propose a 3D U-Net architecture to directly identify pairs of voxels that are pre- and postsynaptic to each other. To that end, we formulate the problem of synaptic partner identification as a classification problem on long-range edges between voxels to encode both the presence of a synaptic pair and its direction. This formulation allows us to directly learn from synaptic point annotations instead of more ex- pensive voxel-based synaptic cleft or vesicle annotations. We evaluate our method on the MICCAI 2016 CREMI challenge and improve over the current state of the art, producing 3% fewer errors than the next best method.

In this paper, we present a novel error measure to compare a computer-generated segmentation of images or volumes against ground truth. This measure, which we call Tolerant Edit Distance (TED), is motivated by two observations that we usually encounter in biomedical image processing: (1) Some errors, like small boundary shifts, are tolerable in practice. Which errors are tolerable is application dependent and should be explicitly expressible in the measure. (2) Non-tolerable errors have to be corrected manually. The effort needed to do so should be reflected by the error measure. Our measure is the minimal weighted sum of split and merge operations to apply to one segmentation such that it resembles another segmentation within specified tolerance bounds. This is in contrast to other commonly used measures like Rand index or variation of information, which integrate small, but tolerable, differences. Additionally, the TED provides intuitive numbers and allows the localization and classification of errors in images or volumes. We demonstrate the applicability of the TED on 3D segmentations of neurons in electron microscopy images where topological correctness is arguable more important than exact boundary locations. Furthermore, we show that the TED is not just limited to evaluation tasks. We use it as the loss function in a max-margin learning framework to find parameters of an automatic neuron segmentation algorithm. We show that training to minimize the TED, i.e., to minimize crucial errors, leads to higher segmentation accuracy compared to other learning methods.

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.

Tracking all nuclei of an embryo in noisy and dense fluorescence microscopy data is a challenging task. We build upon a recent method for nuclei tracking that combines weakly-supervised learning from a small set of nuclei center point annotations with an integer linear program (ILP) for optimal cell lineage extraction. Our work specifically addresses the following challenging properties of C. elegans embryo recordings: (1) Many cell divisions as compared to benchmark recordings of other organisms, and (2) the presence of polar bodies that are easily mistaken as cell nuclei. To cope with (1), we devise and incorporate a learnt cell division detector. To cope with (2), we employ a learnt polar body detector. We further propose automated ILP weights tuning via a structured SVM, alleviating the need for tedious manual set-up of a respective grid search.

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.

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.

No abstract available.

Segmentation of objects in microscopy images is required for many biomedical applications. We introduce object-centric embeddings (OCEs), which embed image patches such that the spatial offsets between patches cropped from the same object are preserved. Those learnt embeddings can be used to delineate individual objects and thus obtain instance segmentations. Here, we show theoretically that, under assumptions commonly found in microscopy images, OCEs can be learnt through a self-supervised task that predicts the spatial offset between image patches. Together, this forms an unsupervised cell instance segmentation method which we evaluate on nine diverse large-scale microscopy datasets. Segmentations obtained with our method lead to substantially improved results, compared to state-of-the-art baselines on six out of nine datasets, and perform on par on the remaining three datasets. If ground-truth annotations are available, our method serves as an excellent starting point for supervised training, reducing the required amount of ground-truth needed by one order of magnitude, thus substantially increasing the practical applicability of our method. Source code is available at github.com/funkelab/cellulus.