XLSTM-HVED: Cross-Modal Brain Tumor Segmentation and MRI Reconstruction Method Using Vision XLSTM and Heteromodal Variational Encoder-Decoder

• URL: http://arxiv.org/abs/2412.07804v2
• Date: Fri, 03 Jan 2025 05:22:41 GMT
• Title: XLSTM-HVED: Cross-Modal Brain Tumor Segmentation and MRI Reconstruction Method Using Vision XLSTM and Heteromodal Variational Encoder-Decoder
• Authors: Shenghao Zhu, Yifei Chen, Shuo Jiang, Weihong Chen, Chang Liu, Yuanhan Wang, Xu Chen, Yifan Ke, Feiwei Qin, Changmiao Wang, Zhu Zhu,
• Abstract summary: We introduce the XLSTM-HVED model, which integrates a heteromodal encoder-decoder framework with the Vision XLSTM module to reconstruct missing MRI modalities. Key innovation of our approach is the Self-Attention Variational (SAVE) module, which improves the integration of modal features. Our experiments using the BraTS 2024 dataset demonstrate that our model significantly outperforms existing advanced methods in handling cases where modalities are missing.
• Score: 9.141615533517719
• License:
• Abstract: Neurogliomas are among the most aggressive forms of cancer, presenting considerable challenges in both treatment and monitoring due to their unpredictable biological behavior. Magnetic resonance imaging (MRI) is currently the preferred method for diagnosing and monitoring gliomas. However, the lack of specific imaging techniques often compromises the accuracy of tumor segmentation during the imaging process. To address this issue, we introduce the XLSTM-HVED model. This model integrates a hetero-modal encoder-decoder framework with the Vision XLSTM module to reconstruct missing MRI modalities. By deeply fusing spatial and temporal features, it enhances tumor segmentation performance. The key innovation of our approach is the Self-Attention Variational Encoder (SAVE) module, which improves the integration of modal features. Additionally, it optimizes the interaction of features between segmentation and reconstruction tasks through the Squeeze-Fusion-Excitation Cross Awareness (SFECA) module. Our experiments using the BraTS 2024 dataset demonstrate that our model significantly outperforms existing advanced methods in handling cases where modalities are missing. Our source code is available at https://github.com/Quanato607/XLSTM-HVED.

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