Efficient Connectivity-Preserving Instance Segmentation with Supervoxel-Based Loss Function

Reconstructing the intricate local morphology of neurons and their long-range projecting axons can address many connectivity related questions in neuroscience. The main bottleneck in connectomics pipelines is correcting topological errors, as multiple entangled neuronal arbors is a challenging instance segmentation problem. More broadly, segmentation of curvilinear, filamentous structures continues to pose significant challenges. To address this problem, we extend the notion of simple points from digital topology to connected sets of voxels (i.e. supervoxels) and propose a topology-aware neural network segmentation method with minimal computational overhead. We demonstrate its effectiveness on a new public dataset of 3-d light microscopy images of mouse brains, along with the benchmark datasets DRIVE, ISBI12, and CrackTree.

Convex Combination Belief Propagation Algorithms

We introduce new message passing algorithms for inference with graphical models. The standard min-sum and sum-product belief propagation algorithms are guaranteed to converge when the graph is tree-structured, but may not converge and can be sensitive to the initialization when the graph contains cycles. This paper describes modifications to the standard belief propagation algorithms that are guaranteed to converge to a unique solution regardless of the topology of the graph.