@article {65001, title = {Automated reconstruction of a serial-section EM drosophila brain with flood-filling networks and local realignment.}, journal = {bioRxiv}, year = {2019}, month = {2019 Apr 11}, abstract = {

Reconstruction of neural circuitry at single-synapse resolution is an attractive target for improving understanding of the nervous system in health and disease. Serial section transmission electron microscopy (ssTEM) is among the most prolific imaging methods employed in pursuit of such reconstructions. We demonstrate how Flood-Filling Networks (FFNs) can be used to computationally segment a forty-teravoxel whole-brain\ Drosophila\ ssTEM volume. To compensate for data irregularities and imperfect global alignment, FFNs were combined with procedures that locally re-align serial sections and dynamically adjust image content. The proposed approach produced a largely merger-free segmentation of the entire ssTEM\ Drosophila\ brain, which we make freely available. As compared to manual tracing using an efficient skeletonization strategy, the segmentation enabled circuit reconstruction and analysis workflows that were an order of magnitude faster.

}, doi = {10.1101/605634}, author = {Peter H. Li and Larry F. Lindsey and Micha{\l} Januszewski and Zhihao Zheng and Alexander Shakeel Bates and Istv{\'a}n Taisz and Mike Tyka and Matthew Nichols and Feng Li and Eric Perlman and Jeremy Maitin-Shepard and Tim Blakely and Laramie Leavitt and Gregory S.X.E. Jefferis and Davi Bock and Viren Jain} } @proceedings {46351, title = {Learning to Agglomerate Superpixel Hierarchies}, volume = {24}, year = {2011}, month = {12/2011}, publisher = {MIT Press}, address = {Granada, Spain}, abstract = {

An agglomerative clustering algorithm merges the most similar pair of clusters at every iteration. The function that evaluates similarity is traditionally hand- designed, but there has been recent interest in supervised or semisupervised settings in which ground-truth clustered data is available for training. Here we show how to train a similarity function by regarding it as the action-value function of a reinforcement learning problem. We apply this general method to segment images by clustering superpixels, an application that we call Learning to Agglomerate Superpixel Hierarchies (LASH). When applied to a challenging dataset of brain images from serial electron microscopy, LASH dramatically improved segmentation accuracy when clustering supervoxels generated by state of the boundary detection algorithms. The naive strategy of directly training only supervoxel similarities and applying single linkage clustering produced less improvement.

}, author = {Viren Jain and Srinivas C. Turaga and K Briggman and Moritz N. Helmstaedter and Winfried Denk and H. S. Seung} }