Eghbali Reza, Nedelec Pierre, Weiss David, Bhalerao Radhika, Xie Long, Rudie Jeffrey D, Liu Chunlei, Sugrue Leo P, Rauschecker Andreas M
Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, USA.
Berkeley Institute for Data Science, University of California, Berkeley, Berkeley, CA, USA.
Neuroinformatics. 2025 Jan 9;23(1):2. doi: 10.1007/s12021-024-09708-z.
This paper introduces the Automated Lesion and Feature Extraction (ALFE) pipeline, an open-source, Python-based pipeline that consumes MR images of the brain and produces anatomical segmentations, lesion segmentations, and human-interpretable imaging features describing the lesions in the brain. ALFE pipeline is modeled after the neuroradiology workflow and generates features that can be used by physicians for quantitative analysis of clinical brain MRIs and for machine learning applications. The pipeline uses a decoupled design which allows the user to customize the image processing, image registrations, and AI segmentation tools without the need to change the business logic of the pipeline. In this manuscript, we give an overview of ALFE, present the main aspects of ALFE pipeline design philosophy, and present case studies.
本文介绍了自动病变与特征提取（ALFE）流程，这是一个基于Python的开源流程，它接收脑部的磁共振成像（MR）图像，并生成解剖分割、病变分割以及描述脑部病变的可被人类解读的成像特征。ALFE流程是仿照神经放射学工作流程建模的，生成的特征可供医生用于临床脑部MRI的定量分析以及机器学习应用。该流程采用解耦设计，允许用户定制图像处理、图像配准和人工智能分割工具，而无需更改流程的业务逻辑。在本手稿中，我们对ALFE进行了概述，介绍了ALFE流程设计理念的主要方面，并展示了案例研究。
