Open Access

ARTICLE

Research on Multimodal Brain Tumor Segmentation Algorithm Based on Feature Decoupling and Information Bottleneck Theory

Xuemei Yang¹, Yuting Zhou², Shiqi Liu¹, Junping Yin^2,3,*

1 China Academy of Engineering Physics, Graduate School, Beijing, 100193, China
2 Shanghai Zhangjiang Institute of Mathematics, Biomedical Laboratory, Shanghai, 201203, China
3 Institute of Applied Physics and Computational Mathematics, Tang Chuang Centre, Beijing, 100094, China

* Corresponding Author: Junping Yin. Email:

(This article belongs to the Special Issue: Novel Methods for Image Classification, Object Detection, and Segmentation)

Computers, Materials & Continua 2025, 82(2), 3281-3307. https://doi.org/10.32604/cmc.2024.057991

Received 02 September 2024; Accepted 06 December 2024; Issue published 17 February 2025

Abstract

Aiming at the problems of information loss and the relationship between features and target tasks in multimodal medical image segmentation, a multimodal medical image segmentation algorithm based on feature decoupling and information bottleneck theory is proposed in this paper. Based on the reversible network, the bottom-up learning method for different modal information is constructed, which enhances the features’ expression ability and the network’s learning ability. The feature fusion module is designed to balance multi-directional information flow. To retain the information relevant to the target task to the maximum extent and suppress the information irrelevant to the target task, the feature decoupling module is designed to ensure a strong correlation between the feature and the target task. A loss function based on information bottleneck theory was intended to improve information quality and remove redundant information. Based on BraTs2021, BraTs2023-MET and ANNLIB datasets, the proposed algorithm is analyzed qualitatively and quantitatively in this paper. In the quantitative experiment, the Dice coefficient of the proposed algorithm was increased by 0.110 on average compared with other methods, and the HD95 was decreased by 28.568 on average compared with other methods. In qualitative analysis, the proposed algorithm can effectively segment the incoherent region between the lesion and the lesion boundary and achieve accurate segmentation of the lesion.

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Keywords

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