PARSE challenge 2022: Pulmonary Arteries Segmentation using Swin U-Net Transformer(Swin UNETR) and U-Net
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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.
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
5 of 11 references.
References · 5
- 20163D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotationcited by 1,476
- 2022Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Imagescited by 830
- 2021Self-Supervised Pre-Training of Swin Transformers for 3D Medical Image Analysiscited by 422
- 2018DeepVesselNet: Vessel Segmentation, Centerline Prediction, and Bifurcation Detection in 3-D Angiographic Volumescited by 62
- 2021Pulmonary Vessel Segmentation based on Orthogonal Fused U-Net++ of Chest CT Imagescited by 11
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Citing papers per year: 2024: 1; 2025: 0; 2026: 2.
3 papers.