research-article

SCAR U-Net: A 3D Spatial-Channel Attention ResU-Net for Brain Tumor Segmentation

Authors:

Zhenguo NieAuthors Info & Claims

ISAIMS '22: Proceedings of the 3rd International Symposium on Artificial Intelligence for Medicine Sciences

Pages 497 - 501

https://doi.org/10.1145/3570773.3570826

Published: 09 December 2022 Publication History

Abstract

Although surgical resection is the best option for treating gliomas, it might be difficult to minimize the harm done to healthy brain regions. As a result, segmenting brain tumors in medical image analysis has become essential. A number of segmentation investigations utilizing CNNs have lately demonstrated promising performance thanks to the advancement of image equipment and deep learning techniques. In this study, we propose the SCAR U-Net, an end-to-end 3D residual U-Net model incorporating attention mechanisms for the segmentation of brain tumors. The SCAR U-Net employs channel and spatial attention processes and has a 3D U-Net architecture with residual blocks. We evaluate the model on a subset of the BraTS 2021 dataset. And the model outperforms the baseline significantly by ET, TC, and WT in the test set. Finally, we use ablation tests to confirm the beneficial effects of the residual connections and the attention modules.

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Cited By

Pani KChawla I(2025)A hybrid approach for multi modal brain tumor segmentation using two phase transfer learning, SSL and a hybrid 3DUNETComputers and Electrical Engineering10.1016/j.compeleceng.2024.109418118:PBOnline publication date: 7-Jan-2025
https://dl.acm.org/doi/10.1016/j.compeleceng.2024.109418
Chi MAn HJin XNie Z(2024)An N-Shaped Lightweight Network with a Feature Pyramid and Hybrid Attention for Brain Tumor SegmentationEntropy10.3390/e2602016626:2(166)Online publication date: 15-Feb-2024
https://doi.org/10.3390/e26020166

Index Terms

SCAR U-Net: A 3D Spatial-Channel Attention ResU-Net for Brain Tumor Segmentation
1. Applied computing
  1. Life and medical sciences
    1. Bioinformatics
2. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
        Image segmentation

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cover image ACM Other conferences

ISAIMS '22: Proceedings of the 3rd International Symposium on Artificial Intelligence for Medicine Sciences

October 2022

594 pages

ISBN:9781450398442

DOI:10.1145/3570773

Copyright © 2022 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 09 December 2022

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The National Key Research and Development Program of China

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ISAIMS 2022

ISAIMS 2022: 2022 3rd International Symposium on Artificial Intelligence for Medicine Sciences

October 13 - 15, 2022

Amsterdam, Netherlands

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Overall Acceptance Rate 53 of 112 submissions, 47%

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Cited By

Pani KChawla I(2025)A hybrid approach for multi modal brain tumor segmentation using two phase transfer learning, SSL and a hybrid 3DUNETComputers and Electrical Engineering10.1016/j.compeleceng.2024.109418118:PBOnline publication date: 7-Jan-2025
https://dl.acm.org/doi/10.1016/j.compeleceng.2024.109418
Chi MAn HJin XNie Z(2024)An N-Shaped Lightweight Network with a Feature Pyramid and Hybrid Attention for Brain Tumor SegmentationEntropy10.3390/e2602016626:2(166)Online publication date: 15-Feb-2024
https://doi.org/10.3390/e26020166

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