arXiv Open Access 2022

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

Akansh Maurya Kunal Dashrath Patil Rohan Padhy Kalluri Ramakrishna Ganapathy Krishnamurthi

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Abstrak

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.

Topik & Kata Kunci

eess.IV cs.CV

Penulis (5)

Akansh Maurya

Kunal Dashrath Patil

Rohan Padhy

Kalluri Ramakrishna

Ganapathy Krishnamurthi

Format Sitasi

APA MLA BibTeX

Maurya, A., Patil, K.D., Padhy, R., Ramakrishna, K., Krishnamurthi, G. (2022). PARSE challenge 2022: Pulmonary Arteries Segmentation using Swin U-Net Transformer(Swin UNETR) and U-Net. https://arxiv.org/abs/2208.09636

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Informasi Jurnal

Tahun Terbit: 2022
Bahasa: en
Sumber Database: arXiv
Akses: Open Access ✓