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ORIGINAL RESEARCH article

Front. Neurosci.

Sec. Brain Imaging Methods

This article is part of the Research TopicAdvancing neuroimaging diagnostics with machine learning and computational modelsView all 3 articles

Detection of leptomeningeal angiomas in brain MRI of Sturge-Weber syndrome using multi-scale multi-scan Mamba

Provisionally accepted
Weiqun  BaoWeiqun Bao1Chenghao  XueChenghao Xue2Ruisheng  SuRuisheng Su3Xindan  HuXindan Hu2Yuanning  LiYuanning Li2Xiaoqiang  WangXiaoqiang Wang1Tao  TanTao Tan4Dake  HeDake He1Lin  XuLin Xu2*
  • 1Shanghai Jiaotong University School of Medicine Xinhua Hospital, Shanghai, China
  • 2ShanghaiTech University, Shanghai, China
  • 3Technische Universiteit Eindhoven, Eindhoven, Netherlands
  • 4Macao Polytechnic University, Macau, Macao, SAR China

The final, formatted version of the article will be published soon.

Objectives: Sturge-Weber syndrome (SWS) is a congenital neurological disorder occurring in the early childhood. Timely diagnosis of SWS is essential for proper medical intervention that prevents the development of various neurological issues. Leptomeningeal angiomas (LA) are the clinical manifestation of SWS. Detection of LA is currently performed by manual inspection of the magnetic resonance images (MRI) by experienced neurologist, which is time-consuming and lack of inter-rater consistency. The aim of the present study is to investigate automated LA detection in MRI of SWS patients. Methods: A Mamba-based encoder-decoder architecture was employed in the present study. Particularly, a multi-scale multi-scan strategy was proposed to convert 3-D volume into 1-D sequence, enabling capturing long-range dependency with reduced computation complexity. Our dataset consists of 40 SWS patients with T1-enhanced MRI. The proposed model was first pre-trained on a public brain tumor segmentation (BraTS) dataset and then fine-tuned and tested on the SWS dataset using 5-fold cross validation. Results and Conclusion: Our results show excellent performance of the proposed method, e.g., Dice score of 91.53% and 78.67% for BraTS and SWS, respectively, outperforming several state-of-the-art methods as well as two neurologists. Mamba-based deep learning method can automatically identify LA in MRI images, enabling automated SWS diagnosis in clinical settings.

Keywords: Sturge-Weber Syndrome, Leptomeningeal angiomas, Magnetic Resonance Imaging, Mamba, Multi-scale Multi-scan

Received: 05 Sep 2025; Accepted: 27 Oct 2025.

Copyright: © 2025 Bao, Xue, Su, Hu, Li, Wang, Tan, He and Xu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Lin Xu, xulin1@shanghaitech.edu.cn

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