Brain tumor segmentation with attention-based U-Net

Tuofu Li; Javin Jia Liu; Yintao Tai; Yuxuan Tian

doi:10.1117/12.2623112

1 December 2021 Brain tumor segmentation with attention-based U-Net

Tuofu Li, Javin Jia Liu, Yintao Tai, Yuxuan Tian

Proceedings Volume 12079, Second IYSF Academic Symposium on Artificial Intelligence and Computer Engineering; 120790N (2021) https://doi.org/10.1117/12.2623112
Event: 2nd IYSF Academic Symposium on Artificial Intelligence and Computer Engineering, 2021, Xi'an, China

Abstract

Brain tumors are a hazardous type of tumor, and they build pressure inside the skull when they grow, which can potentially cause brain damage or even death. Attention mechanisms are widely adopted in state-of-the-art deep learning architectures for computer vision and neural translation tasks since they enhance networks' ability to capture spatial and channel-wise relationships. We offer an attention-based image segmentation model that outlines the brain tumors in Magnetic Resonance Imaging (MRI) scans if present. In the paper, we mainly focus on integrating Squeeze-and-Excitation Block and CBAM into the commonly used segmentation model, U-Net, to resolve the problem of concatenating unnecessary information into the decoder blocks and attempt to locate the tumor boundaries. Our research clearly shows the application of the attention mechanism in U-Net, incorporates the Squeeze-and-Excitation with CBAM, and improves the performance in the brain tumor segmentation task. The model is delivered on an app with additional text to speech and chatbot features provided.

Citation Download Citation

Tuofu Li, Javin Jia Liu, Yintao Tai, and Yuxuan Tian "Brain tumor segmentation with attention-based U-Net", Proc. SPIE 12079, Second IYSF Academic Symposium on Artificial Intelligence and Computer Engineering, 120790N (1 December 2021); https://doi.org/10.1117/12.2623112

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KEYWORDS

Image segmentation

Tumors

Brain

Magnetic resonance imaging

Data modeling

Performance modeling

Image classification

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