Efficient Connectivity-Preserving Instance Segmentation with Supervoxel-Based Loss Function
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Abstract (arXiv)
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.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 13 of its 30 references that are in the map. First posted on arXiv 2 Jan 2025.
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13 of 30 references.
References · 13
- 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
- 2020clDice -- A Novel Topology-Preserving Loss Function for Tubular Structure Segmentationcited by 286
- 2019Topology-Preserving Deep Image Segmentationcited by 198
- 2017Deep Learning with Topological Signaturescited by 113
- 2017Beyond the Pixel-Wise Loss for Topology-Aware Delineationcited by 82
- 2021Topology-Aware Segmentation Using Discrete Morse Theorycited by 75
- 2018A Topological Regularizer for Classifiers via Persistent Homologycited by 62
- 2019Connectivity-Optimized Representation Learning via Persistent Homologycited by 43
- 2019Explicit topological priors for deep-learning based image segmentation using persistent homologycited by 30
- 2021Structure-Aware Image Segmentation with Homotopy Warpingcited by 24
- 2022Learning Probabilistic Topological Representations Using Discrete Morse Theorycited by 20
- 2019Convolutional nets for reconstructing neural circuits from brain images acquired by serial section electron microscopycited by 6
1 more
- 2019Reconstructing neuronal anatomy from whole-brain imagescited by 1
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Citing papers per year: 2025: 3.
3 papers.