@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 {47152, title = {Synaptic circuits and their variations within different columns in the visual system of Drosophila.}, journal = {Proceedings of the National Academy of Sciences of the United States of America}, volume = {112}, year = {2015}, month = {2015 Nov 3}, pages = {13711-6}, abstract = {

We reconstructed the synaptic circuits of seven columns in the second neuropil or medulla behind the fly{\textquoteright}s compound eye. These neurons embody some of the most stereotyped circuits in one of the most miniaturized of animal brains. The reconstructions allow us, for the first time to our knowledge, to study variations between circuits in the medulla{\textquoteright}s neighboring columns. This variation in the number of synapses and the types of their synaptic partners has previously been little addressed because methods that visualize multiple circuits have not resolved detailed connections, and existing connectomic studies, which can see such connections, have not so far examined multiple reconstructions of the same circuit. Here, we address the omission by comparing the circuits common to all seven columns to assess variation in their connection strengths and the resultant rates of several different and distinct types of connection error. Error rates reveal that, overall, \<1\% of contacts are not part of a consensus circuit, and we classify those contacts that supplement (E+) or are missing from it (E-). Autapses, in which the same cell is both presynaptic and postsynaptic at the same synapse, are occasionally seen; two cells in particular, Dm9 and Mi1, form >=20-fold more autapses than do other neurons. These results delimit the accuracy of developmental events that establish and normally maintain synaptic circuits with such precision, and thereby address the operation of such circuits. They also establish a precedent for error rates that will be required in the new science of connectomics.

}, issn = {1091-6490}, doi = {10.1073/pnas.1509820112}, author = {Takemura, Shin-ya and Xu, C Shan and Lu, Zhiyuan and Rivlin, Patricia K and Parag, Toufiq and Olbris, Donald J and Plaza, Stephen and Zhao, Ting and Katz, William T and Umayam, Lowell and Weaver, Charlotte and Hess, Harald F and Horne, Jane Anne and Nunez-Iglesias, Juan and Aniceto, Roxanne and Chang, Lei-Ann and Lauchie, Shirley and Nasca, Ashley and Ogundeyi, Omotara and Sigmund, Christopher and Takemura, Satoko and Tran, Julie and Langille, Carlie and Le Lacheur, Kelsey and McLin, Sari and Shinomiya, Aya and Chklovskii, Dmitri B and Meinertzhagen, Ian A and Scheffer, Louis K} } @article {6934, title = {Graph-based active learning of agglomeration (GALA): a Python library to segment 2D and 3D neuroimages}, journal = {Frontiers in Neuroinformatics}, volume = {8}, year = {2014}, month = {2014 Apr 4}, pages = {34}, abstract = {

The aim in high-resolution connectomics is to reconstruct complete neuronal connectivity in a tissue. Currently, the only technology capable of resolving the smallest neuronal processes is electron microscopy (EM). Thus, a common approach to network reconstruction is to perform (error-prone) automatic segmentation of EM images, followed by manual proofreading by experts to fix errors. We have developed an algorithm and software library to not only improve the accuracy of the initial automatic segmentation, but also point out the image coordinates where it is likely to have made errors. Our software, called gala (graph-based active learning of agglomeration), improves the state of the art in agglomerative image segmentation. It is implemented in Python and makes extensive use of the scientific Python stack (numpy, scipy, networkx, scikit-learn, scikit-image, and others). We present here the software architecture of the gala library, and discuss several designs that we consider would be generally useful for other segmentation packages. We also discuss the current limitations of the gala library and how we intend to address them.

}, issn = {1662-5196}, doi = {10.3389/fninf.2014.00034}, author = {Nunez-Iglesias, Juan and Kennedy, Ryan and Plaza, Stephen M and Chakraborty, Anirban and William T. Katz} } @article {6676, title = {Electron microscopy reconstruction of brain structure using sparse representations over learned dictionaries.}, journal = {IEEE Transactions on Medical Imaging}, volume = {32}, year = {2013}, month = {2013 Aug 2}, pages = {2179-88}, abstract = {

A central problem in neuroscience is reconstructing neuronal circuits on the synapse level. Due to a wide range of scales in brain architecture such reconstruction requires imaging that is both high-resolution and high-throughput. Existing electron microscopy (EM) techniques possess required resolution in the lateral plane and either high-throughput or high depth resolution but not both. Here, we exploit recent advances in unsupervised learning and signal processing to obtain high depth-resolution EM images computationally without sacrificing throughput. First, we show that the brain tissue can be represented as a sparse linear combination of localized basis functions that are learned using high-resolution datasets. We then develop compressive sensing-inspired techniques that can reconstruct the brain tissue from very few (typically 5) tomographic views of each section. This enables tracing of neuronal processes and, hence, high throughput reconstruction of neural circuits on the level of individual synapses.

}, issn = {1558-254X}, doi = {10.1109/TMI.2013.2276018}, author = {Hu, Tao and Nunez-Iglesias, Juan and Vitaladevuni, Shiv and Scheffer, Lou and Xu, Shan and Bolorizadeh, Mehdi and Hess, Harald and Fetter, Richard and Chklovskii, Dmitri} } @article {6263, title = {Machine learning of hierarchical clustering to segment 2D and 3D images.}, journal = {PLoS One}, volume = {8}, year = {2013}, month = {2013}, pages = {e71715}, abstract = {

We aim to improve segmentation through the use of machine learning tools during region agglomeration. We propose an active learning approach for performing hierarchical agglomerative segmentation from superpixels. Our method combines multiple features at all scales of the agglomerative process, works for data with an arbitrary number of dimensions, and scales to very large datasets. We advocate the use of variation of information to measure segmentation accuracy, particularly in 3D electron microscopy (EM) images of neural tissue, and using this metric demonstrate an improvement over competing algorithms in EM and natural images.

}, issn = {1932-6203}, doi = {10.1371/journal.pone.0071715}, author = {Nunez-Iglesias, Juan and Kennedy, Ryan and Toufiq Parag and Shi, Jianbo and Chklovskii, Dmitri B} } @article {6505, title = {Betamax: towards optimal sampling strategies for high-throughput screens.}, journal = {Journal of Computational Biology: A Journal of Computational Molecular Cell Biology}, volume = {19}, year = {2012}, month = {2012 Jun}, pages = {776-84}, abstract = {

Sample size is a critical component in the design of any high-throughput genetic screening approach. Sample size determination from assumptions or limited data at the planning stages, though standard practice, may at times be unreliable because of the difficulty of a priori modeling of effect sizes and variance. Methods to update the sample size estimate during the course of the study could improve statistical power. In this article, we introduce an approach to estimate the power and update it continuously during the screen. We use this estimate to decide where to sample next to achieve maximum overall statistical power. Finally, in simulations, we demonstrate significant gains in study recall over the naive strategy of equal sample sizes while maintaining the same total number of samples.

}, keywords = {Algorithms, Computer Simulation, Genetic Testing, High-Throughput Screening Assays, Humans, Models, Research Design, Sample Size, Statistical}, issn = {1557-8666}, doi = {10.1089/cmb.2012.0036}, author = {Grover, Dhruv and Nunez-Iglesias, Juan} } @article {4325, title = {High resolution segmentation of neuronal tissues from low depth-resolution EM imagery.}, journal = {8th International Conference of Energy Minimization Methods in Computer Vision and Pattern Recognition Energy Minimization Methods in Computer Vision and Pattern Recognition}, volume = {6819}, year = {2011}, month = {2011}, pages = {261-72}, abstract = {

The challenge of recovering the topology of massive neuronal circuits can potentially be met by high throughput Electron Microscopy (EM) imagery. Segmenting a 3-dimensional stack of EM images into the individual neurons is difficult, due to the low depth-resolution in existing high-throughput EM technology, such as serial section Transmission EM (ssTEM). In this paper we propose methods for detecting the high resolution locations of membranes from low depth-resolution images. We approach this problem using both a method that learns a discriminative, over-complete dictionary and a kernel SVM. We test this approach on tomographic sections produced in simulations from high resolution Focused Ion Beam (FIB) images and on low depth-resolution images acquired with ssTEM and evaluate our results by comparing it to manual labeling of this data.

}, issn = {978-3-642-23093-6}, author = {Glasner, Daniel and Hu, Tao and Nunez-Iglesias, Juan and Scheffer, Lou and Xu, CS and Hess, Harald and Fetter, Richard and Chklovskii, Dmitri and Basri, Ronen} }