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

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 (this https URL), 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.
View on arXiv@article{kofler2025_2506.13807, title={ BraTS orchestrator : Democratizing and Disseminating state-of-the-art brain tumor image analysis }, author={ Florian Kofler and Marcel Rosier and Mehdi Astaraki and Ujjwal Baid and Hendrik Möller and Josef A. Buchner and Felix Steinbauer and Eva Oswald and Ezequiel de la Rosa and Ivan Ezhov and Constantin von See and Jan Kirschke and Anton Schmick and Sarthak Pati and Akis Linardos and Carla Pitarch and Sanyukta Adap and Jeffrey Rudie and Maria Correia de Verdier and Rachit Saluja and Evan Calabrese and Dominic LaBella and Mariam Aboian and Ahmed W. Moawad and Nazanin Maleki and Udunna Anazodo and Maruf Adewole and Marius George Linguraru and Anahita Fathi Kazerooni and Zhifan Jiang and Gian Marco Conte and Hongwei Li and Juan Eugenio Iglesias and Spyridon Bakas and Benedikt Wiestler and Marie Piraud and Bjoern Menze }, journal={arXiv preprint arXiv:2506.13807}, year={ 2025 } }