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

2025-07-11

AI Summary

Brain lesion analysis faces challenges including complex processing pipelines, poor cross-modal compatibility, and high development barriers. Method: This paper proposes a flexible, extensible, modular analysis framework that uniformly supports registration, skull-stripping, lesion segmentation, and quantitative assessment across multi-modal MRI (T1, T2, FLAIR, T1c). It innovatively integrates missing-modality synthesis and lesion inpainting modules to reduce cognitive load in algorithm deployment. Built upon BraTS benchmark practices, the framework incorporates co-registration, atlas alignment, and Panoptica-based lesion-level metric computation, enabling Python-based pipeline construction. Results: Experimental validation demonstrates robust automated analysis performance on gliomas, brain metastases, and multiple sclerosis lesions. The framework exhibits strong generalizability and is readily transferable to other biomedical image analysis tasks.

Technology Category

• Computer Vision: Multi-modal Vision
• Humans and AI: Brain-Sensing and Analysis
• Machine Learning: Multimodal Learning

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for heterogeneous, signed, attributed, multi-relational, temporal, higher-order, and annotated Web-related graphs

Abstract

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.

Problem

• Provides modular Python toolkit for brain lesion image analysis
• Simplifies complex workflows with adaptable preprocessing and modality synthesis
• Enables performance quantification for segmentation models in clinical practice

Innovation

• Modular Python toolkit for brain lesion analysis
• Adaptable preprocessing for multi-modal images
• Leverages BraTS algorithms for missing modalities