# Topology Repairing of Disconnected Pulmonary Airways and Vessels: Baselines and a Dataset

arXiv: https://arxiv.org/abs/2306.07089 · HTML: https://mlsanity.com/paper/2306.07089/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 28 references that are in the map.
- First posted on arXiv 12 Jun 2023.

## Builds on (most cited first, 15 of 15)

- [Deep High-Resolution Representation Learning for Visual Recognition](https://mlsanity.com/paper/1908.07919/) (2019)
- [Deep High-Resolution Representation Learning for Human Pose Estimation](https://mlsanity.com/paper/1902.09212/) (2019)
- [Simple Baselines for Human Pose Estimation and Tracking](https://mlsanity.com/paper/1804.06208/) (2018)
- [Rethinking the Heatmap Regression for Bottom-up Human Pose Estimation](https://mlsanity.com/paper/2012.15175/) (2020)
- [DeepVesselNet: Vessel Segmentation, Centerline Prediction, and Bifurcation Detection in 3-D Angiographic Volumes](https://mlsanity.com/paper/1803.09340/) (2018)
- [One-Shot Medical Landmark Detection](https://mlsanity.com/paper/2103.04527/) (2021)
- [Learning Tubule-Sensitive CNNs for Pulmonary Airway and Artery-Vein Segmentation in CT](https://mlsanity.com/paper/2012.05767/) (2020)
- [RibSeg Dataset and Strong Point Cloud Baselines for Rib Segmentation from CT Scans](https://mlsanity.com/paper/2109.09521/) (2021)
- [Automatic airway segmentation from Computed Tomography using robust and efficient 3-D convolutional neural networks](https://mlsanity.com/paper/2103.16328/) (2021)
- [NaviAirway: a Bronchiole-sensitive Deep Learning-based Airway Segmentation Pipeline](https://mlsanity.com/paper/2203.04294/) (2022)
- [Heatmap Regression via Randomized Rounding](https://mlsanity.com/paper/2009.00225/) (2020)
- [Multi-site, Multi-domain Airway Tree Modeling (ATM'22): A Public Benchmark for Pulmonary Airway Segmentation](https://mlsanity.com/paper/2303.05745/) (2023)
- [What Makes for Automatic Reconstruction of Pulmonary Segments](https://mlsanity.com/paper/2207.03078/) (2022)
- [Neural Annotation Refinement: Development of a New 3D Dataset for Adrenal Gland Analysis](https://mlsanity.com/paper/2206.15328/) (2022)
- [Deep 3D Vessel Segmentation based on Cross Transformer Network](https://mlsanity.com/paper/2208.10148/) (2022)

## Built on by (most cited first, 6 of 6)

- [MedShapeNet -- A Large-Scale Dataset of 3D Medical Shapes for Computer Vision](https://mlsanity.com/paper/2308.16139/) (2023)
- [Efficient Anatomical Labeling of Pulmonary Tree Structures via Deep Point-Graph Representation-based Implicit Fields](https://mlsanity.com/paper/2309.17329/) (2023)
- [Interpolation-Split: a data-centric deep learning approach with big interpolated data to boost airway segmentation performance](https://mlsanity.com/paper/2308.00008/) (2023)
- [Template-Guided Reconstruction of Pulmonary Segments with Neural Implicit Functions](https://mlsanity.com/paper/2505.08919/) (2025)
- [Rethinking the Detail-Preserved Completion of Complex Tubular Structures based on Point Cloud: a Dataset and a Benchmark](https://mlsanity.com/paper/2508.17658/) (2025)
- [Learning Topology-Aware Implicit Field for Unified Pulmonary Tree Modeling with Incomplete Topological Supervision](https://mlsanity.com/paper/2602.02186/) (2026)

## Abstract (arXiv)

Accurate segmentation of pulmonary airways and vessels is crucial for the diagnosis and treatment of pulmonary diseases. However, current deep learning approaches suffer from disconnectivity issues that hinder their clinical usefulness. To address this challenge, we propose a post-processing approach that leverages a data-driven method to repair the topology of disconnected pulmonary tubular structures. Our approach formulates the problem as a keypoint detection task, where a neural network is trained to predict keypoints that can bridge disconnected components. We use a training data synthesis pipeline that generates disconnected data from complete pulmonary structures. Moreover, the new Pulmonary Tree Repairing (PTR) dataset is publicly available, which comprises 800 complete 3D models of pulmonary airways, arteries, and veins, as well as the synthetic disconnected data. Our code and data are available at https://github.com/M3DV/pulmonary-tree-repairing.