MIST工具箱助力脑瘤分割，灵活后处理提升精度。
Pre- and Post-Treatment Glioma Segmentation with the Medical Imaging Segmentation Toolkit
- 构建可定制的模块化后处理框架，支持多种图像操作
- 三种策略中复杂分类管道表现最优，符合挑战排名标准
- 开源可扩展，适合需要快速迭代的医学图像研究者
医学图像分割技术快速发展，但方法间的严格比较仍因缺乏标准化和可定制工具而困难。本文介绍医学图像分割工具箱（MIST）当前状态，重点展示其为BraTS 2025前/后治疗脑胶质瘤分割挑战设计的灵活、模块化后处理框架。自2024年BraTS成人脑胶质瘤后处理分割挑战首次发布以来，MIST的后处理模块已显著扩展，支持多种变换，包括小对象移除或替换、最大连通域提取，以及孔洞填充、闭运算等形态学操作。这些变换可组合成用户自定义策略，实现对最终分割结果的精细控制。我们评估了三种策略——从简单的小对象移除到更复杂的类别特定流水线——并使用BraTS排名协议进行性能排序。结果表明，MIST能促进快速实验与针对性优化，最终生成符合BraTS 2025挑战要求的高质量分割结果。MIST持续开源且可扩展，支持可复现、可扩展的医学图像分割研究。
原文摘要 · Abstract (English)
Medical image segmentation continues to advance rapidly, yet rigorous comparison between methods remains challenging due to a lack of standardized and customizable tooling. In this work, we present the current state of the Medical Imaging Segmentation Toolkit (MIST), with a particular focus on its flexible and modular postprocessing framework designed for the BraTS 2025 pre- and post-treatment glioma segmentation challenge. Since its debut in the 2024 BraTS adult glioma post-treatment segmentation challenge, MIST's postprocessing module has been significantly extended to support a wide range of transforms, including removal or replacement of small objects, extraction of the largest connected components, and morphological operations such as hole filling and closing. These transforms can be composed into user-defined strategies, enabling fine-grained control over the final segmentation output. We evaluate three such strategies - ranging from simple small-object removal to more complex, class-specific pipelines - and rank their performance using the BraTS ranking protocol. Our results highlight how MIST facilitates rapid experimentation and targeted refinement, ultimately producing high-quality segmentations for the BraTS 2025 challenge. MIST remains open source and extensible, supporting reproducible and scalable research in medical image segmentation.
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