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

arXiv: https://arxiv.org/abs/2501.01022 · HTML: https://mlsanity.com/paper/2501.01022/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 30 references that are in the map.
- First posted on arXiv 2 Jan 2025.

## Builds on (most cited first, 13 of 13)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [clDice -- A Novel Topology-Preserving Loss Function for Tubular Structure Segmentation](https://mlsanity.com/paper/2003.07311/) (2020)
- [Topology-Preserving Deep Image Segmentation](https://mlsanity.com/paper/1906.05404/) (2019)
- [Deep Learning with Topological Signatures](https://mlsanity.com/paper/1707.04041/) (2017)
- [Beyond the Pixel-Wise Loss for Topology-Aware Delineation](https://mlsanity.com/paper/1712.02190/) (2017)
- [Topology-Aware Segmentation Using Discrete Morse Theory](https://mlsanity.com/paper/2103.09992/) (2021)
- [A Topological Regularizer for Classifiers via Persistent Homology](https://mlsanity.com/paper/1806.10714/) (2018)
- [Connectivity-Optimized Representation Learning via Persistent Homology](https://mlsanity.com/paper/1906.09003/) (2019)
- [Explicit topological priors for deep-learning based image segmentation using persistent homology](https://mlsanity.com/paper/1901.10244/) (2019)
- [Structure-Aware Image Segmentation with Homotopy Warping](https://mlsanity.com/paper/2112.07812/) (2021)
- [Learning Probabilistic Topological Representations Using Discrete Morse Theory](https://mlsanity.com/paper/2206.01742/) (2022)
- [Convolutional nets for reconstructing neural circuits from brain images acquired by serial section electron microscopy](https://mlsanity.com/paper/1904.12966/) (2019)
- [Reconstructing neuronal anatomy from whole-brain images](https://mlsanity.com/paper/1903.07027/) (2019)

## Built on by (most cited first, 3 of 3)

- [ICDAR 2025 Competition on FEw-Shot Text line segmentation of ancient handwritten documents (FEST)](https://mlsanity.com/paper/2509.12965/) (2025)
- [Improve Retinal Artery/Vein Classification via Channel Couplin](https://mlsanity.com/paper/2508.03738/) (2025)
- [Few-Shot Connectivity-Aware Text Line Segmentation in Historical Documents](https://mlsanity.com/paper/2508.19162/) (2025)

## 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.