用视觉XLSTM和异模编码器提升脑肿瘤分割与缺失MRI重建效果
XLSTM-HVED: Cross-Modal Brain Tumor Segmentation and MRI Reconstruction Method Using Vision XLSTM and Heteromodal Variational Encoder-Decoder
- 融合视觉XLSTM与异模编码器，跨模态联合优化
- 在BraTS 2024上实现缺失模态下分割性能显著超越现有方法
- 适合医学图像分析、多模态重建与脑瘤智能诊断研究者
神经胶质瘤是侵袭性最强的癌症之一，因其生物学行为不可预测，在治疗与监测中面临巨大挑战。磁共振成像（MRI）是目前诊断和监测胶质瘤的首选方法，但缺乏特异性成像技术常导致肿瘤分割不准确。为此，我们提出XLSTM-HVED模型，将异模编码器-解码器框架与视觉XLSTM模块结合，用于重建缺失的MRI模态。通过深度融合空间与时间特征，提升肿瘤分割性能。其核心创新在于自注意力变分编码器（SAVE）模块，增强了模态特征整合能力；同时，通过压缩-融合-激励跨注意力（SFECA）模块优化了分割与重建任务间的特征交互。在BraTS 2024数据集上的实验表明，该模型在模态缺失情况下显著优于现有先进方法。源代码已公开于https://github.com/Quanato607/XLSTM-HVED。
原文摘要 · Abstract (English)
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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