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

TL;DR

This paper introduces a topology-aware neural network segmentation method that uses supervoxels to improve the accuracy of segmenting complex curvilinear structures like neurons, with minimal computational cost.

Contribution

It extends digital topology concepts to supervoxels and develops a novel loss function for connectivity-preserving segmentation in 3D microscopy images.

Findings

Effective on a new mouse brain dataset

Performs well on benchmark datasets

Maintains topology with minimal overhead

Abstract

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.