BraTS orchestrator : Democratizing and Disseminating state-of-the-art brain tumor image analysis
This work addresses the problem of disseminating advanced brain tumor image analysis tools to broader neuro-radiology and neuro-oncology communities, though it is incremental as it focuses on packaging existing methods.
The paper tackles the limited adoption of state-of-the-art brain tumor segmentation and synthesis algorithms from the BraTS challenge by introducing BraTS orchestrator, an open-source Python package that provides seamless access to these algorithms, enabling researchers and clinicians to easily deploy them for inference with minimal programming experience.
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.