@article {67220, title = {Anisotropic EM Segmentation by 3D Affinity Learning and Agglomeration}, journal = {CoRR}, volume = {abs/1707.08935}, year = {2017}, abstract = {

The field of connectomics has recently produced neuron wiring diagrams from relatively large brain regions from multiple animals. Most of these neural reconstructions were computed from isotropic (e.g., FIBSEM) or near isotropic (e.g., SBEM) data. In spite of the remarkable progress on algorithms in recent years, automatic dense reconstruction from anisotropic data remains a challenge for the connectomics community. One significant hurdle in the segmentation of anisotropic data is the difficulty in generating a suitable initial over-segmentation. In this study, we present a segmentation method for anisotropic EM data that agglomerates a 3D over-segmentation computed from the 3D affinity prediction. A 3D U-net is trained to predict 3D affinities by the MALIS approach. Experiments on multiple datasets demonstrates the strength and robustness of the proposed method for anisotropic EM segmentation.

}, url = {https://arxiv.org/abs/1707.08935v2}, author = {Toufiq Parag and Fabian Tschopp and William Grisaitis and Srinivas C. Turaga and Xuewen Zhang and Brian Matejek and Lee Kamentsky and Jeff W. Lichtman and Hanspeter Pfister} } @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} }