# Morphology Edge Attention Network and Optimal Geometric Matching Connection model for vascular segmentation

arXiv: https://arxiv.org/abs/2306.01808 · HTML: https://mlsanity.com/paper/2306.01808/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 17 of its 35 references that are in the map.
- First posted on arXiv 2 Jun 2023.

## Builds on (most cited first, 17 of 17)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [Non-local Neural Networks](https://mlsanity.com/paper/1711.07971/) (2017)
- [Attention U-Net: Learning Where to Look for the Pancreas](https://mlsanity.com/paper/1804.03999/) (2018)
- [clDice -- A Novel Topology-Preserving Loss Function for Tubular Structure Segmentation](https://mlsanity.com/paper/2003.07311/) (2020)
- [A Topological Loss Function for Deep-Learning based Image Segmentation using Persistent Homology](https://mlsanity.com/paper/1910.01877/) (2019)
- [Topology-Aware Segmentation Using Discrete Morse Theory](https://mlsanity.com/paper/2103.09992/) (2021)
- [DeepVesselNet: Vessel Segmentation, Centerline Prediction, and Bifurcation Detection in 3-D Angiographic Volumes](https://mlsanity.com/paper/1803.09340/) (2018)
- [Learning Topological Interactions for Multi-Class Medical Image Segmentation](https://mlsanity.com/paper/2207.09654/) (2022)
- [Dense Dilated Network with Probability Regularized Walk for Vessel Detection](https://mlsanity.com/paper/1910.12010/) (2019)
- [Global Minimum for a Finsler Elastica Minimal Path Approach](https://mlsanity.com/paper/1612.00343/) (2016)
- [Learning Hybrid Representations for Automatic 3D Vessel Centerline Extraction](https://mlsanity.com/paper/2012.07262/) (2020)
- [Trajectory Grouping with Curvature Regularization for Tubular Structure Tracking](https://mlsanity.com/paper/2003.03710/) (2020)
- [Divergence Prior and Vessel-tree Reconstruction](https://mlsanity.com/paper/1811.09745/) (2018)
- [Confluent Vessel Trees with Accurate Bifurcations](https://mlsanity.com/paper/2103.14268/) (2021)
- [Enforcing connectivity of 3D linear structures using their 2D projections](https://mlsanity.com/paper/2207.06832/) (2022)

## Abstract (arXiv)

There are many unsolved problems in vascular image segmentation, including vascular structural connectivity, scarce branches and missing small vessels. Obtaining vessels that preserve their correct topological structures is currently a crucial research issue, as it provides an overall view of one vascular system. In order to preserve the topology and accuracy of vessel segmentation, we proposed a novel Morphology Edge Attention Network (MEA-Net) for the segmentation of vessel-like structures, and an Optimal Geometric Matching Connection (OGMC) model to connect the broken vessel segments. The MEA-Net has an edge attention module that improves the segmentation of edges and small objects by morphology operation extracting boundary voxels on multi-scale. The OGMC model uses the concept of curve touching from differential geometry to filter out fragmented vessel endpoints, and then employs minimal surfaces to determine the optimal connection order between blood vessels. Finally, we calculate the geodesic to repair missing vessels under a given Riemannian metric. Our method achieves superior or competitive results compared to state-of-the-art methods on four datasets of 3D vascular segmentation tasks, both effectively reducing vessel broken and increasing vessel branch richness, yielding blood vessels with a more precise topological structure.