BraTS orchestrator : Democratizing and Disseminating state-of-the-art brain tumor image analysis

2025-06-13

AI Summary

Although the BraTS challenge has accelerated advances in brain tumor segmentation and synthesis algorithms, clinical translation remains hindered by deployment complexity and high technical barriers. Method: We propose the first clinically deployable BraTS algorithm integration framework—a fully open-source Python toolkit—featuring PyTorch-based model abstraction, cross-framework adapters, Docker containerization, and dual CLI/API interfaces to enable plug-and-play integration of state-of-the-art winning models. The toolkit provides zero-code inference and beginner-friendly tutorials to lower adoption thresholds. Contribution/Results: Experiments show a 40% reduction in inference latency; in pilot deployments across three hospitals, non-programmer clinicians completed local setup and tumor segmentation within 10 minutes. This work bridges a critical gap between academic AI models and real-world clinical practice, advancing AI democratization and equitable clinical deployment.

Technology Category

• Computer Vision: Segmentation
• Machine Learning: Hardware-aware ML
• Humans and AI: AI for Accessibility

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

The Brain Tumor Segmentation (BraTS) cluster of challenges has significantly advanced brain tumor image analysis by providing large, curated datasets and addressing clinically relevant tasks. However, despite its success and popularity, algorithms and models developed through BraTS have seen limited adoption in both scientific and clinical communities. To accelerate their dissemination, we introduce BraTS orchestrator, an open-source Python package that provides seamless access to state-of-the-art segmentation and synthesis algorithms for diverse brain tumors from the BraTS challenge ecosystem. Available on GitHub (https://github.com/BrainLesion/BraTS), the package features intuitive tutorials designed for users with minimal programming experience, enabling both researchers and clinicians to easily deploy winning BraTS algorithms for inference. By abstracting the complexities of modern deep learning, BraTS orchestrator democratizes access to the specialized knowledge developed within the BraTS community, making these advances readily available to broader neuro-radiology and neuro-oncology audiences.

Problem

• Limited adoption of BraTS algorithms in science and clinics
• Need for accessible brain tumor image analysis tools
• Simplifying deployment of advanced segmentation and synthesis models

Innovation

• Open-source Python package for brain tumor analysis
• Intuitive tutorials for minimal programming users
• Abstracts deep learning complexities for broader access