# PARSE challenge 2022: Pulmonary Arteries Segmentation using Swin U-Net Transformer(Swin UNETR) and U-Net

arXiv: https://arxiv.org/abs/2208.09636 · HTML: https://mlsanity.com/paper/2208.09636/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 11 references that are in the map.
- First posted on arXiv 20 Aug 2022.

## Builds on (most cited first, 5 of 5)

- [3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation](https://mlsanity.com/paper/1606.06650/) (2016)
- [Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Images](https://mlsanity.com/paper/2201.01266/) (2022)
- [Self-Supervised Pre-Training of Swin Transformers for 3D Medical Image Analysis](https://mlsanity.com/paper/2111.14791/) (2021)
- [DeepVesselNet: Vessel Segmentation, Centerline Prediction, and Bifurcation Detection in 3-D Angiographic Volumes](https://mlsanity.com/paper/1803.09340/) (2018)
- [Pulmonary Vessel Segmentation based on Orthogonal Fused U-Net++ of Chest CT Images](https://mlsanity.com/paper/2107.01502/) (2021)

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

- [A Geometric Algorithm for Blood Vessel Reconstruction from Skeletal Representation](https://mlsanity.com/paper/2402.12797/) (2024)
- [Learning Dynamic Collaborative Network for Semi-supervised 3D Vessel Segmentation](https://mlsanity.com/paper/2601.07377/) (2026)
- [MorVess: Morphology-Aware Pulmonary Vessel Segmentation Network](https://mlsanity.com/paper/2606.24214/) (2026)

## Abstract (arXiv)

In this work, we present our proposed method to segment the pulmonary arteries from the CT scans using Swin UNETR and U-Net-based deep neural network architecture. Six models, three models based on Swin UNETR, and three models based on 3D U-net with residual units were ensemble using a weighted average to make the final segmentation masks. Our team achieved a multi-level dice score of 84.36 percent through this method. The code of our work is available on the following link: https://github.com/akansh12/parse2022. This work is part of the MICCAI PARSE 2022 challenge.