@article {68841, title = {Accelerated EM Connectome Reconstruction using 3D Visualization and Segmentation Graphs}, journal = {bioRxiv}, year = {2020}, month = {2020 Jan 17}, abstract = {

Recent advances in automatic image segmentation and synapse prediction in electron microscopy (EM) datasets of the brain enable more efficient reconstruction of neural connectivity. In these datasets, a single neuron can span thousands of images containing complex tree-like arbors with thousands of synapses. While image segmentation algorithms excel within narrow fields of views, the algorithms sometimes struggle to correctly segment large neurons, which require large context given their size and complexity. Conversely, humans are comparatively good at reasoning with large objects. In this paper, we introduce several semi-automated strategies that combine 3D visualization and machine guidance to accelerate connectome reconstruction. In particular, we introduce a strategy to quickly correct a segmentation through merging and\ cleaving, or splitting a segment along supervoxel boundaries, with both operations driven by affinity scores in the underlying segmentation. We deploy these algorithms as streamlined workflows in a tool called\ Neu3\ and demonstrate superior performance compared to prior work, thus enabling efficient reconstruction of much larger datasets. The insights into proofreading from our work clarify the trade-offs to consider when tuning the parameters of image segmentation algorithms.

}, doi = {10.1101/2020.01.17.909572}, url = {https://doi.org/10.1101/2020.01.17.909572}, author = {Philip M. Hubbard and Stuart Berg and Ting Zhao and Donald J. Olbris and Lowell Umayam and Jeremy Maitin-Shepard and Michal Januszewski and William T. Katz and Erika R. Neace and Stephen M. Plaza} } @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} }