Brain tumor MR image segmentation based on self-integrated U-Net network

Tianqi Lin; Shuiling Zeng

doi:10.1117/12.3011979

7 December 2023 Brain tumor MR image segmentation based on self-integrated U-Net network

Tianqi Lin, Shuiling Zeng

Proceedings Volume 12941, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2023); 129414A (2023) https://doi.org/10.1117/12.3011979
Event: Third International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 203), 2023, Yinchuan, China

Abstract

In view of the limitations of traditional fusion methods in U-Net networks due to the requirement for a large amount of memory and resulting in low segmentation accuracy caused by insufficient labeled samples, an algorithm for brain tumor MR image segmentation based on a self-integrated segmentation network structure of U-Net is proposed. A self-integrated segmentation network module based on recursive fusion method is introduced into the U-Net network to address the issue of low segmentation accuracy caused by insufficient labeled samples and excessive memory consumption. The proposed algorithm is evaluated using brain tumor MR image data provided by BraTS (the brain tumor image segmentation challenge), and metrics such as Dice similarity coefficient are used for evaluation. The results show that the Dice values for complete tumor, tumor core, and enhancing tumor can reach 0.84, 0.85, and 0.78 respectively, which is an improvement compared to the U-Net model. Experimental results demonstrate that the proposed algorithm effectively enhances the accuracy of brain tumor segmentation with good segmentation performance.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Tianqi Lin and Shuiling Zeng "Brain tumor MR image segmentation based on self-integrated U-Net network", Proc. SPIE 12941, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2023), 129414A (7 December 2023); https://doi.org/10.1117/12.3011979

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KEYWORDS

Image segmentation

Tumors

Brain

Neuroimaging

Education and training

Magnetic resonance imaging

Image enhancement

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