arXiv:2507.09036cs.CVcs.AI2025-07被引 3

一站式脑病变影像分析工具,轻松构建复杂流程

BrainLesion Suite: A Flexible and User-Friendly Framework for Modular Brain Lesion Image Analysis

  • 模块化设计,支持多模态图像自动配准与预处理
  • 基于BraTS算法生成缺失模态和病灶分割结果
  • 适合医学影像研究者快速搭建分析流水线

BrainLesion Suite 是一个基于 Python 的灵活工具包,用于构建模块化的脑病变影像分析流程。遵循 Python 设计哲学,它提供低认知负担的开发体验,简化临床与科研中的复杂工作流构建。核心包含可适应的预处理模块,支持任意多模态输入图像的配准、图谱注册,以及可选的去颅骨和去脸处理。该框架利用来自 BraTS 挑战赛的算法,实现缺失模态合成、病灶修复及病理特异性肿瘤分割。同时内置性能评估工具(如 panoptica),可计算病灶级别的评价指标。尽管最初面向胶质瘤、转移瘤和多发性硬化等脑病变分析,但也可拓展至其他生物医学影像任务。各组件与教程均开源在 GitHub。

原文摘要 · Abstract (English)

BrainLesion Suite is a versatile toolkit for building modular brain lesion image analysis pipelines in Python. Following Pythonic principles, BrainLesion Suite is designed to provide a 'brainless' development experience, minimizing cognitive effort and streamlining the creation of complex workflows for clinical and scientific practice. At its core is an adaptable preprocessing module that performs co-registration, atlas registration, and optional skull-stripping and defacing on arbitrary multi-modal input images. BrainLesion Suite leverages algorithms from the BraTS challenge to synthesize missing modalities, inpaint lesions, and generate pathology-specific tumor segmentations. BrainLesion Suite also enables quantifying segmentation model performance, with tools such as panoptica to compute lesion-wise metrics. Although BrainLesion Suite was originally developed for image analysis pipelines of brain lesions such as glioma, metastasis, and multiple sclerosis, it can be adapted for other biomedical image analysis applications. The individual BrainLesion Suite packages and tutorials are accessible on GitHub.

影像分析脑病变Python工具

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