🤖 AI Summary
In neural connectomics, accurate axon reconstruction is hindered by topological errors—such as fragmentation and merging—in instance segmentation of highly entangled filamentous structures. To address this, we propose a lightweight topology-aware segmentation method. Our approach introduces, for the first time, the digital topology concept of “simple points” to supervoxel-level connected components, enabling a supervoxel-based topological constraint loss that preserves connectivity while maintaining computational efficiency. Integrated within a 3D U-Net architecture, this loss is coupled with digital-topology-guided connectivity regularization. Evaluated on a novel mouse brain light-sheet microscopy dataset and established benchmarks (DRIVE, ISBI12, CrackTree), our method significantly reduces fragmentation and merging errors, improves instance-level connectivity accuracy, and incurs negligible computational overhead. The key contributions are: (i) the generalization of simple-point theory to supervoxels; (ii) a differentiable, topology-preserving loss; and (iii) a computationally efficient framework achieving state-of-the-art topological fidelity in filamentous structure segmentation.
📝 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.