专为脑膜瘤放疗规划设计的交互式分割工具，提升临床精度。
Domain-Specialized Interactive Segmentation Framework for Meningioma Radiotherapy Planning
- 融合点、框、自由绘线等交互方式，支持医生协同标注。
- 在500例MRI上达到77.6%的Dice系数和64.8%的IoU。
- 针对放疗场景定制，适合神经肿瘤临床工作流使用。
精确勾画脑膜瘤对有效放疗（RT）规划至关重要，直接影响治疗效果与周围健康组织保护。尽管自动化深度学习方法已展现出巨大潜力，但因肿瘤异质性，实现一致的临床级分割仍具挑战。交互式医学图像分割（IMIS）通过结合先进AI与临床输入应对该难题。然而，通用分割工具虽适用广泛，却常缺乏脑膜瘤放疗规划这类关键病种所需的特异性。为此，我们提出Interactive-MEN-RT，一款专为放疗流程中医生辅助3D脑膜瘤分割设计的交互式分割工具。系统集成点标注、边界框、套索及草图等多种临床友好交互方式，提升可用性与精度。在包含500例对比增强T1加权MRI的BraTS 2025脑膜瘤放疗分割挑战赛评估中，Interactive-MEN-RT显著优于其他方法，最高达77.6%的Dice相似系数与64.8%的交并比。结果表明，在脑膜瘤放疗规划等关键应用中，需采用临床定制化分割方案。代码已公开：https://github.com/snuh-rad-aicon/Interactive-MEN-RT。
原文摘要 · Abstract (English)
Precise delineation of meningiomas is crucial for effective radiotherapy (RT) planning, directly influencing treatment efficacy and preservation of adjacent healthy tissues. While automated deep learning approaches have demonstrated considerable potential, achieving consistently accurate clinical segmentation remains challenging due to tumor heterogeneity. Interactive Medical Image Segmentation (IMIS) addresses this challenge by integrating advanced AI techniques with clinical input. However, generic segmentation tools, despite widespread applicability, often lack the specificity required for clinically critical and disease-specific tasks like meningioma RT planning. To overcome these limitations, we introduce Interactive-MEN-RT, a dedicated IMIS tool specifically developed for clinician-assisted 3D meningioma segmentation in RT workflows. The system incorporates multiple clinically relevant interaction methods, including point annotations, bounding boxes, lasso tools, and scribbles, enhancing usability and clinical precision. In our evaluation involving 500 contrast-enhanced T1-weighted MRI scans from the BraTS 2025 Meningioma RT Segmentation Challenge, Interactive-MEN-RT demonstrated substantial improvement compared to other segmentation methods, achieving Dice similarity coefficients of up to 77.6\% and Intersection over Union scores of 64.8\%. These results emphasize the need for clinically tailored segmentation solutions in critical applications such as meningioma RT planning. The code is publicly available at: https://github.com/snuh-rad-aicon/Interactive-MEN-RT
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