XLSTM-HVED: Cross-Modal Brain Tumor Segmentation and MRI Reconstruction Method Using Vision XLSTM and Heteromodal Variational Encoder-Decoder
Journal:
arXiv
Published Date:
Dec 9, 2024
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.
