标题： BrainLesion 套件：一种灵活且用户友好的模块化脑损伤图像分析框架 显示英文标题

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

摘要： BrainLesion 套件是一个功能强大的工具包，用于在 Python 中构建模块化的脑部病变图像分析流程。 遵循 Python 的原则，BrainLesion 套件旨在提供一种“无脑”的开发体验，最大限度地减少认知努力，并简化临床和科研实践中复杂工作流的创建。 其核心是一个可适应的预处理模块，能够对任意多模态输入图像进行配准、图谱配准，并可选地进行去颅骨和去面部处理。 BrainLesion 套件利用 BraTS 挑战中的算法来合成缺失的模态，填补病变区域，并生成特定病理的肿瘤分割。 BrainLesion 套件还支持量化分割模型的性能，例如使用 panoptica 工具计算病灶级别的指标。 尽管 BrainLesion 套件最初是为脑部病变（如胶质瘤、转移瘤和多发性硬化症）的图像分析流程开发的，但它也可以适应其他生物医学图像分析应用。 BrainLesion 套件的各个单独包和教程可在 GitHub 上访问。 显示英文摘要

摘要： 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.