arXiv 2507.090362025-07-11cs.CVcs.AIcs.LGcs.SE

BrainLesion Suite：一个用于模块化脑部病灶图像分析的灵活且用户友好的框架

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

Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland（苏黎世大学定量生物医学系）；Helmholtz AI, Helmholtz Zentrum München, Germany（海德堡人工智能，慕尼黑研究中心）；Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Germany（医学诊断与介入神经放射学系，慕尼黑技术大学）；TranslaTUM - Central Institute for Translational Cancer Research, Technical University of Munich, Germany（TranslaTUM - 转化癌症研究中央研究所，慕尼黑技术大学）；Chair for AI in Medicine, Klinikum Rechts der Isar, Munich, Germany（医学人工智能系，慕尼黑Klinikum Rechts der Isar）；Department of Radiation Oncology, Klinikum rechts der Isar, Technical University of Munich, Germany（放射肿瘤学系，慕尼黑Klinikum Rechts der Isar）；Institute of Clinical Neuroimmunology, Faculty of Medicine, Ludwig Maximilian University, Munich, Germany（临床神经免疫学研究所，路德维希-马克西米利安大学）；School of Computation, Information and Technology, Technical University of Munich, Germany（计算、信息与技术学院，慕尼黑技术大学）；Division of Computational Pathology, Indiana School of Medicine, Indianapolis, IN, USA（计算病理学系，印第安纳医学院）；Research Center for Digital Technologies in Dentistry and CAD/CAM, Department of Dentistry, Faculty of Medicine and Dentistry, Danube Private University, Krems, Austria（牙科数字技术研究中心，医学与牙科学院，多瑙私人大学）；Medical Research Group, MLCommons, San Francisco, CA, USA（医学研究组，MLCommons，旧金山，美国）；Department of Neurology, Clinical Neuroscience Center and Brain Tumor Center, University and University Hospital Zurich, 8091 Zurich, Switzerland（神经学系，临床神经科学中心和脑肿瘤中心，苏黎世大学及苏黎世大学医院）；Department of Medical Radiation Physics, Stockholm University, Solna, Sweden（医学辐射物理学系，斯德哥尔摩大学）；Department of Oncology-Pathology, Karolinska Institutet, Solna, Sweden, Sweden（肿瘤病理学系，卡罗林斯卡研究所）；Division of Oncology and Children's Research Center, University Children's Hospital Zurich, Switzerland（肿瘤与儿童研究中心，苏黎世儿童医院）；Wallace H. Coulter Department of Biomedical Engineering at Georgia Tech and Emory University, Atlanta, USA（Wallace H. Coulter生物医学工程系，佐治亚理工学院和埃默里大学，美国）；Institute of Radiation Medicine (IRM), Helmholtz Zentrum München (HMGU), Germany（放射医学研究所（IRM），海德堡研究中心（HMGU））；German Consortium for Translational Cancer Research (DKTK), Partner Site Munich, Munich, Germany（德国转化癌症研究联盟（DKTK），慕尼黑合作伙伴站点）

Florian Kofler, Marcel Rosier, Mehdi Astaraki, Hendrik Möller, Ilhem Isra Mekki, Josef A. Buchner, Anton Schmick, Arianna Pfiffer, Eva Oswald, Lucas Zimmer, Eze… 展开作者

Florian Kofler, Marcel Rosier, Mehdi Astaraki, Hendrik Möller, Ilhem Isra Mekki, Josef A. Buchner, Anton Schmick, Arianna Pfiffer, Eva Oswald, Lucas Zimmer, Ezequiel de la Rosa, Sarthak Pati, Julian Canisius, Arianna Piffer, Ujjwal Baid, Mahyar Valizadeh, Akis Linardos, Jan C. Peeken, Surprosanna Shit, Felix Steinbauer, Daniel Rueckert, Rolf Heckemann, Spyridon Bakas, Jan Kirschke, Constantin von See, Ivan Ezhov, Marie Piraud, Benedikt Wiestler, Bjoern Menze

备注：: 16p, 3f

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AI总结：

本文提出BrainLesion Suite，一个基于Python的模块化脑部病灶图像分析框架，通过自适应预处理、缺失模态合成和病灶分割等核心功能，简化了临床与科研中的复杂工作流。

摘要：

BrainLesion Suite是一个用于在Python中构建模块化脑部病灶图像分析流程的多功能工具包。遵循Pythonic原则，BrainLesion Suite旨在提供“无脑”的开发体验，最大程度减少认知负担，并简化临床与科学实践中复杂工作流的创建。其核心是一个自适应预处理模块，可对任意多模态输入图像执行配准、图谱配准，以及可选的颅骨剥离和去脸处理。BrainLesion Suite利用来自BraTS挑战赛的算法来合成缺失模态、修复病灶，并生成特定病理的肿瘤分割。它还支持量化分割模型性能，并提供了如panoptica等工具来计算基于病灶的指标。尽管它最初是为分析胶质瘤、转移瘤和多发性硬化等脑部病灶而开发的，但也可适配于其他生物医学图像分析应用。各个包和教程可在GitHub上获取。

英文摘要：

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.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。