Electrical Engineering and Systems Science > Image and Video Processing
arXiv:2412.07804 (eess)
[Submitted on 9 Dec 2024 (v1), last revised 5 Mar 2025 (this version, v3)]
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
View a PDF of the paper titled XLSTM-HVED: Cross-Modal Brain Tumor Segmentation and MRI Reconstruction Method Using Vision XLSTM and Heteromodal Variational Encoder-Decoder, by Shenghao Zhu and 10 other authors
View PDFHTML (experimental)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 atthis https URL.
| Comments: | 5 pages, 2 figures |
| Subjects: | Image and Video Processing (eess.IV); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV) |
| Cite as: | arXiv:2412.07804 [eess.IV] |
| (orarXiv:2412.07804v3 [eess.IV] for this version) | |
| https://doi.org/10.48550/arXiv.2412.07804 arXiv-issued DOI via DataCite | |
| Journal reference: | ISBI 2025 |
Submission history
From: Yifei Chen [view email][v1] Mon, 9 Dec 2024 09:04:02 UTC (677 KB)
[v2] Fri, 3 Jan 2025 05:22:41 UTC (677 KB)
[v3] Wed, 5 Mar 2025 10:09:25 UTC (677 KB)
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View a PDF of the paper titled XLSTM-HVED: Cross-Modal Brain Tumor Segmentation and MRI Reconstruction Method Using Vision XLSTM and Heteromodal Variational Encoder-Decoder, by Shenghao Zhu and 10 other authors
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