# Pulmonary Vessel Segmentation based on Orthogonal Fused U-Net++ of Chest CT Images

arXiv: https://arxiv.org/abs/2107.01502 · HTML: https://mlsanity.com/paper/2107.01502/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 14 references that are in the map.
- First posted on arXiv 3 Jul 2021.

## Builds on (most cited first, 3 of 3)

- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [UNet++: A Nested U-Net Architecture for Medical Image Segmentation](https://mlsanity.com/paper/1807.10165/) (2018)
- [DeepVesselNet: Vessel Segmentation, Centerline Prediction, and Bifurcation Detection in 3-D Angiographic Volumes](https://mlsanity.com/paper/1803.09340/) (2018)

## Built on by (most cited first, 11 of 11)

- [ReCal-Net: Joint Region-Channel-Wise Calibrated Network for Semantic Segmentation in Cataract Surgery Videos](https://mlsanity.com/paper/2109.12448/) (2021)
- [DeepPyramid: Enabling Pyramid View and Deformable Pyramid Reception for Semantic Segmentation in Cataract Surgery Videos](https://mlsanity.com/paper/2207.01453/) (2022)
- [Learning Low-Rank Feature for Thorax Disease Classification](https://mlsanity.com/paper/2404.18933/) (2024)
- [PARSE challenge 2022: Pulmonary Arteries Segmentation using Swin U-Net Transformer(Swin UNETR) and U-Net](https://mlsanity.com/paper/2208.09636/) (2022)
- [Extremely weakly-supervised blood vessel segmentation with physiologically based synthesis and domain adaptation](https://mlsanity.com/paper/2305.17054/) (2023)
- [ToNNO: Tomographic Reconstruction of a Neural Network's Output for Weakly Supervised Segmentation of 3D Medical Images](https://mlsanity.com/paper/2404.13103/) (2024)
- [Bronchovascular Tree-Guided Weakly Supervised Learning Method for Pulmonary Segment Segmentation](https://mlsanity.com/paper/2505.13911/) (2025)
- [DeepPyram: Enabling Pyramid View and Deformable Pyramid Reception for Semantic Segmentation in Cataract Surgery Videos](https://mlsanity.com/paper/2109.05352/) (2021)
- [ClamNet: Using contrastive learning with variable depth Unets for medical image segmentation](https://mlsanity.com/paper/2206.05225/) (2022)
- [Template-Guided Reconstruction of Pulmonary Segments with Neural Implicit Functions](https://mlsanity.com/paper/2505.08919/) (2025)
- [A Self-training Framework for Semi-supervised Pulmonary Vessel Segmentation and Its Application in COPD](https://mlsanity.com/paper/2507.19074/) (2025)

## Abstract (arXiv)

Pulmonary vessel segmentation is important for clinical diagnosis of pulmonary diseases, while is also challenging due to the complicated structure. In this work, we present an effective framework and refinement process of pulmonary vessel segmentation from chest computed tomographic (CT) images. The key to our approach is a 2.5D segmentation network applied from three orthogonal axes, which presents a robust and fully automated pulmonary vessel segmentation result with lower network complexity and memory usage compared to 3D networks. The slice radius is introduced to convolve the adjacent information of the center slice and the multi-planar fusion optimizes the presentation of intra- and inter- slice features. Besides, the tree-like structure of the pulmonary vessel is extracted in the post-processing process, which is used for segmentation refining and pruning. In the evaluation experiments, three fusion methods are tested and the most promising one is compared with the state-of-the-art 2D and 3D structures on 300 cases of lung images randomly selected from LIDC dataset. Our method outperforms other network structures by a large margin and achieves by far the highest average DICE score of 0.9272 and precision of 0.9310, as per our knowledge from the pulmonary vessel segmentation models available in the literature.