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

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arxiv 2506.13807 v1 pith:LKLVO6AL submitted 2025-06-13 eess.IV cs.AIcs.CV

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

Florian Kofler , Marcel Rosier , Mehdi Astaraki , Ujjwal Baid , Hendrik M\"oller , Josef A. Buchner , Felix Steinbauer , Eva Oswald
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Ezequiel de la Rosa Ivan Ezhov Constantin von See Jan Kirschke Anton Schmick Sarthak Pati Akis Linardos Carla Pitarch Sanyukta Adap Jeffrey Rudie Maria Correia de Verdier Rachit Saluja Evan Calabrese Dominic LaBella Mariam Aboian Ahmed W. Moawad Nazanin Maleki Udunna Anazodo Maruf Adewole Marius George Linguraru Anahita Fathi Kazerooni Zhifan Jiang Gian Marco Conte Hongwei Li Juan Eugenio Iglesias Spyridon Bakas Benedikt Wiestler Marie Piraud Bjoern Menze
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classification eess.IV cs.AIcs.CV
keywords bratsbrainalgorithmsorchestratortumoraccessanalysisavailable
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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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.

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Cited by 4 Pith papers

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  2. Improving Pre-trained Adult Glioma Segmentation Models Using only Post-processing Techniques

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    Adaptive post-processing refines outputs from pre-trained glioma segmentation models, improving challenge metrics by 14.9% in one task and 0.9% in another.

  3. Post-Processing Methods for Improving Accuracy in MRI Inpainting

    cs.CV 2025-10 unverdicted novelty 4.0

    Ensembling inpainting models with median filtering, histogram matching, pixel averaging, and lightweight U-Net refinement yields more anatomically plausible and accurate inpainted MRI regions than individual baseline models.

  4. Improving Pre-trained Adult Glioma Segmentation Models Using only Post-processing Techniques

    cs.CV 2025-12 conditional novelty 3.0

    Radiomics-guided thresholds that delete small components and relabel swapped tissue classes improved the BraTS 2025 ranking metric by 14.9% (SSA) and 0.9% (GLI) with zero GPU hours.