@article {67842, title = {CONFIRMS: A Toolkit for Scalable, Black Box Connectome Assessment and Investigation.}, journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society}, volume = {2021}, year = {2021}, month = {2021 Nov 01}, pages = {2444-2450}, abstract = {

The nanoscale connectomics community has recently generated automated and semi-automated "wiring diagrams" of brain subregions from terabytes and petabytes of dense 3D neuroimagery. This process involves many challenging and imperfect technical steps, including dense 3D image segmentation, anisotropic nonrigid image alignment and coregistration, and pixel classification of each neuron and their individual synaptic connections. As data volumes continue to grow in size, and connectome generation becomes increasingly commonplace, it is important that the scientific community is able to rapidly assess the quality and accuracy of a connectome product to promote dataset analysis and reuse. In this work, we share our scalable toolkit for assessing the quality of a connectome reconstruction via targeted inquiry and large-scale graph analysis, and to provide insights into how such connectome proofreading processes may be improved and optimized in the future. We illustrate the applications and ecosystem on a recent reference dataset.Clinical relevance- Large-scale electron microscopy (EM) data offers a novel opportunity to characterize etiologies and neurological diseases and conditions at an unprecedented scale. EM is useful for low-level analyses such as biopsies; this increased scale offers new possibilities for research into areas such as neural networks if certain bottlenecks and problems are overcome.

}, keywords = {Connectome, Ecosystem, Imaging, Three-Dimensional, Microscopy, Electron, Neurons}, issn = {2694-0604}, doi = {10.1109/EMBC46164.2021.9630109}, author = {Bishop, Caitlyn and Matelsky, Jordan and Wilt, Miller and Downs, Joseph and Rivlin, Patricia and Plaza, Stephen and Wester, Brock and Gray-Roncal, William} } @article {47537, title = {Rapid and semi-automated extraction of neuronal cell bodies and nuclei from electron microscopy image stacks.}, journal = {Methods in molecular biology (Clifton, N.J.)}, volume = {1427}, year = {2016}, month = {2016}, pages = {277-90}, abstract = {

Connectomics-the study of how neurons wire together in the brain-is at the forefront of modern neuroscience research. However, many connectomics studies are limited by the time and precision needed to correctly segment large volumes of electron microscopy (EM) image data. We present here a semi-automated segmentation pipeline using freely available software that can significantly decrease segmentation time for extracting both nuclei and cell bodies from EM image volumes.

}, issn = {1940-6029}, doi = {10.1007/978-1-4939-3615-1_16}, author = {Holcomb, Paul S and Morehead, Michael and Doretto, Gianfranco and Chen, Peter and Berg, Stuart and Plaza, Stephen and Spirou, George} } @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} } @proceedings {46298, title = {Small sample learning of superpixel classifiers for EM segmentation.}, volume = {17}, year = {2014}, month = {2014}, pages = {389-97}, abstract = {

Pixel and superpixel classifiers have become essential tools for EM segmentation algorithms. Training these classifiers remains a major bottleneck primarily due to the requirement of completely annotating the dataset which is tedious, error-prone and costly. In this paper, we propose an interactive learning scheme for the superpixel classifier for EM segmentation. Our algorithm is {\textquoteright}active semi-supervised{\textquoteright} because it requests the labels of a small number of examples from user and applies label propagation technique to generate these queries. Using only a small set (\< 20\%) of all datapoints, the proposed algorithm consistently generates a classifier almost as accurate as that estimated from a complete groundtruth. We provide segmentation results on multiple datasets to show the strength of these classifiers.

}, keywords = {Algorithms, Artificial Intelligence, Cells, Cultured, Humans, Image Enhancement, Image Interpretation, Computer-Assisted, Imaging, Three-Dimensional, Microscopy, Electron, Transmission, Neurites, Pattern Recognition, Automated, Reproducibility of Results, Sample Size, Sensitivity and Specificity}, author = {Parag, Toufiq and Plaza, Stephen and Scheffer, Louis} }