BraTS orchestrator : Democratizing and Disseminating state-of-the-art brain tumor image analysis
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arXiv
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| Natura: | Preprint |
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2025
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| _version_ | 1866912433260462080 |
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| author | Kofler, Florian Rosier, Marcel Astaraki, Mehdi Baid, Ujjwal Möller, Hendrik Buchner, Josef A. Steinbauer, Felix Oswald, Eva de la Rosa, Ezequiel Ezhov, Ivan von See, Constantin Kirschke, Jan Schmick, Anton Pati, Sarthak Linardos, Akis Pitarch, Carla Adap, Sanyukta Rudie, Jeffrey de Verdier, Maria Correia Saluja, Rachit Calabrese, Evan LaBella, Dominic Aboian, Mariam Moawad, Ahmed W. Maleki, Nazanin Anazodo, Udunna Adewole, Maruf Linguraru, Marius George Kazerooni, Anahita Fathi Jiang, Zhifan Conte, Gian Marco Li, Hongwei Iglesias, Juan Eugenio Bakas, Spyridon Wiestler, Benedikt Piraud, Marie Menze, Bjoern |
| author_facet | Kofler, Florian Rosier, Marcel Astaraki, Mehdi Baid, Ujjwal Möller, Hendrik Buchner, Josef A. Steinbauer, Felix Oswald, Eva de la Rosa, Ezequiel Ezhov, Ivan von See, Constantin Kirschke, Jan Schmick, Anton Pati, Sarthak Linardos, Akis Pitarch, Carla Adap, Sanyukta Rudie, Jeffrey de Verdier, Maria Correia Saluja, Rachit Calabrese, Evan LaBella, Dominic Aboian, Mariam Moawad, Ahmed W. Maleki, Nazanin Anazodo, Udunna Adewole, Maruf Linguraru, Marius George Kazerooni, Anahita Fathi Jiang, Zhifan Conte, Gian Marco Li, Hongwei Iglesias, Juan Eugenio Bakas, Spyridon Wiestler, Benedikt Piraud, Marie Menze, Bjoern |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_13807 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | BraTS orchestrator : Democratizing and Disseminating state-of-the-art brain tumor image analysis Kofler, Florian Rosier, Marcel Astaraki, Mehdi Baid, Ujjwal Möller, Hendrik Buchner, Josef A. Steinbauer, Felix Oswald, Eva de la Rosa, Ezequiel Ezhov, Ivan von See, Constantin Kirschke, Jan Schmick, Anton Pati, Sarthak Linardos, Akis Pitarch, Carla Adap, Sanyukta Rudie, Jeffrey de Verdier, Maria Correia Saluja, Rachit Calabrese, Evan LaBella, Dominic Aboian, Mariam Moawad, Ahmed W. Maleki, Nazanin Anazodo, Udunna Adewole, Maruf Linguraru, Marius George Kazerooni, Anahita Fathi Jiang, Zhifan Conte, Gian Marco Li, Hongwei Iglesias, Juan Eugenio Bakas, Spyridon Wiestler, Benedikt Piraud, Marie Menze, Bjoern Image and Video Processing Artificial Intelligence Computer Vision and Pattern Recognition 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. |
| title | BraTS orchestrator : Democratizing and Disseminating state-of-the-art brain tumor image analysis |
| topic | Image and Video Processing Artificial Intelligence Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2506.13807 |