# TopNet: Topology Preserving Metric Learning for Vessel Tree Reconstruction and Labelling

arXiv: https://arxiv.org/abs/2009.08674 · HTML: https://mlsanity.com/paper/2009.08674/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Sep 2020.

## Built on by (most cited first, 10 of 10)

- [Modeling and hexahedral meshing of cerebral arterial networks from centerlines](https://mlsanity.com/paper/2201.08279/) (2022)
- [A topology-preserving three-stage framework for fully-connected coronary artery extraction](https://mlsanity.com/paper/2504.01597/) (2025)
- [Extremely weakly-supervised blood vessel segmentation with physiologically based synthesis and domain adaptation](https://mlsanity.com/paper/2305.17054/) (2023)
- [Contour Flow Constraint: Preserving Global Shape Similarity for Deep Learning based Image Segmentation](https://mlsanity.com/paper/2504.09384/) (2025)
- [CoRe: An Automated Pipeline for The Prediction of Liver Resection Complexity from Preoperative CT Scans](https://mlsanity.com/paper/2210.08318/) (2022)
- [Image To Tree with Recursive Prompting](https://mlsanity.com/paper/2301.00447/) (2023)
- [Improving Vessel Segmentation with Multi-Task Learning and Auxiliary Data Available Only During Model Training](https://mlsanity.com/paper/2509.03975/) (2025)
- [A Patient-Specific Pulmonary Arterial Tree Digital Twin to Extract Pulmonary Embolism Biomarkers](https://mlsanity.com/paper/2605.28217/) (2026)
- [Deep vessel segmentation with joint multi-prior encoding](https://mlsanity.com/paper/2409.12334/) (2024)
- [Enhancing the automatic segmentation and analysis of 3D liver vasculature models](https://mlsanity.com/paper/2411.15778/) (2024)

## Abstract (arXiv)

Reconstructing Portal Vein and Hepatic Vein trees from contrast enhanced abdominal CT scans is a prerequisite for preoperative liver surgery simulation. Existing deep learning based methods treat vascular tree reconstruction as a semantic segmentation problem. However, vessels such as hepatic and portal vein look very similar locally and need to be traced to their source for robust label assignment. Therefore, semantic segmentation by looking at local 3D patch results in noisy misclassifications. To tackle this, we propose a novel multi-task deep learning architecture for vessel tree reconstruction. The network architecture simultaneously solves the task of detecting voxels on vascular centerlines (i.e. nodes) and estimates connectivity between center-voxels (edges) in the tree structure to be reconstructed. Further, we propose a novel connectivity metric which considers both inter-class distance and intra-class topological distance between center-voxel pairs. Vascular trees are reconstructed starting from the vessel source using the learned connectivity metric using the shortest path tree algorithm. A thorough evaluation on public IRCAD dataset shows that the proposed method considerably outperforms existing semantic segmentation based methods. To the best of our knowledge, this is the first deep learning based approach which learns multi-label tree structure connectivity from images.