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

• URL: http://arxiv.org/abs/2208.09636v1
• Date: Sat, 20 Aug 2022 08:49:47 GMT
• Title: PARSE challenge 2022: Pulmonary Arteries Segmentation using Swin U-Net Transformer(Swin UNETR) and U-Net
• Authors: Akansh Maurya, Kunal Dashrath Patil, Rohan Padhy, Kalluri Ramakrishna and Ganapathy Krishnamurthi
• Abstract summary: 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. Our team achieved a multi-level dice score of 84.36 percent through this method.
• Score: 0.10032961794537368
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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