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The Brain Tumor Segmentation (BraTS) Challenge: Local Synthesis of Healthy Brain Tissue via Inpainting

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arxiv 2305.08992 v3 pith:BSGUJGM7 submitted 2023-05-15 eess.IV cs.CVcs.LG

Florian Kofler , Felix Meissen , Felix Steinbauer , Robert Graf , Stefan K Ehrlich , Annika Reinke , Eva Oswald , Diana Waldmannstetter
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Florian Hoelzl Izabela Horvath Oezguen Turgut Suprosanna Shit Christina Bukas Kaiyuan Yang Johannes C. Paetzold Ezequiel de da Rosa Isra Mekki Shankeeth Vinayahalingam Hasan Kassem Juexin Zhang Ke Chen Ying Weng Alicia Durrer Philippe C. Cattin Julia Wolleb M. S. Sadique M. M. Rahman W. Farzana A. Temtam K. M. Iftekharuddin Maruf Adewole Syed Muhammad Anwar Ujjwal Baid Anastasia Janas Anahita Fathi Kazerooni Dominic LaBella Hongwei Bran Li Ahmed W Moawad Gian-Marco Conte Keyvan Farahani James Eddy Micah Sheller Sarthak Pati Alexandros Karagyris Alejandro Aristizabal Timothy Bergquist Verena Chung Russell Takeshi Shinohara Farouk Dako Walter Wiggins Zachary Reitman Chunhao Wang Xinyang Liu Zhifan Jiang Elaine Johanson Zeke Meier Ariana Familiar Christos Davatzikos John Freymann Justin Kirby Michel Bilello Hassan M Fathallah-Shaykh Roland Wiest Jan Kirschke Rivka R Colen Aikaterini Kotrotsou Pamela Lamontagne Daniel Marcus Mikhail Milchenko Arash Nazeri Marc-André Weber Abhishek Mahajan Suyash Mohan John Mongan Christopher Hess Soonmee Cha Javier Villanueva-Meyer Errol Colak Priscila Crivellaro Andras Jakab Abiodun Fatade Olubukola Omidiji Rachel Akinola Lagos O O Olatunji Goldey Khanna John Kirkpatrick Michelle Alonso-Basanta Arif Rashid Miriam Bornhorst Ali Nabavizadeh Natasha Lepore Joshua Palmer Antonio Porras Jake Albrecht Udunna Anazodo Mariam Aboian Evan Calabrese Jeffrey David Rudie Marius George Linguraru Juan Eugenio Iglesias Koen Van Leemput Spyridon Bakas Benedikt Wiestler Ivan Ezhov Marie Piraud Bjoern H Menze
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classification eess.IVcs.CVcs.LG
keywords brainchallengealgorithmshealthyinpaintingbratsimagessegmentation
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A myriad of algorithms for the automatic analysis of brain MR images is available to support clinicians in their decision-making. For brain tumor patients, the image acquisition time series typically starts with an already pathological scan. This poses problems, as many algorithms are designed to analyze healthy brains and provide no guarantee for images featuring lesions. Examples include, but are not limited to, algorithms for brain anatomy parcellation, tissue segmentation, and brain extraction. To solve this dilemma, we introduce the BraTS inpainting challenge. Here, the participants explore inpainting techniques to synthesize healthy brain scans from lesioned ones. The following manuscript contains the task formulation, dataset, and submission procedure. Later, it will be updated to summarize the findings of the challenge. The challenge is organized as part of the ASNR-BraTS MICCAI challenge.

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Cited by 7 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Hierarchical Diffusion Framework for Pseudo-Healthy Brain MRI Inpainting with Enhanced 3D Consistency

    eess.IV 2025-07 conditional novelty 6.0 of 10

    A hierarchical axial-then-coronal 2D diffusion framework with adaptive resampling and tissue-aware attention improves pseudo-healthy brain MRI inpainting and downstream segmentation.

  2. Now You Have My Healthy Attention: A U-DiT for Brain-MRI Inpainting

    cs.CV 2026-07 accept novelty 5.5 of 10

    Healthy-only bottleneck attention plus a contralateral-mirror input raise BraTS-2026 healthy-tissue inpainting to SSIM 0.864 / PSNR 24.7 dB on 219 validation cases.

  3. Local Label-Informed Feature Transfer for Generating Ground-Truth Medical Images: A Comparison of GAN- and Diffusion-Based Approaches

    cs.CV 2026-07 conditional novelty 5.0 of 10

    LLIFT generates semi-synthetic brain MRIs with user-placed lesion-like patches using weak labels, though its image-level FID results do not show the patch itself is pathology-realistic.

  4. fastWDM3D: Fast and Accurate 3D Healthy Tissue Inpainting

    eess.IV 2025-07 conditional novelty 5.0 of 10

    fastWDM3D, a wavelet diffusion model with a variance-preserving noise schedule and reconstruction losses, achieves high-quality 3D brain inpainting in two steps and about 1.81 seconds per image.

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

    eess.IV 2025-06 conditional novelty 4.0 of 10

    BraTS orchestrator is a new open-source package that provides uniform, tutorial-based access to winning BraTS segmentation and synthesis algorithms for brain tumor MRI.

  6. U-Net Based Healthy 3D Brain Tissue Inpainting

    eess.IV 2025-07 conditional novelty 3.0 of 10

    A U-Net with random mask augmentation and a combined MAE plus SSIM loss reconstructs healthy brain tissue in masked MRI scans, achieving an SSIM of 0.841 on the BraTS 2023 inpainting validation set.

  7. BrainLesion Suite: A Flexible and User-Friendly Framework for Modular Brain Lesion Image Analysis

    cs.CV 2025-07 conditional novelty 3.0 of 10

    BrainLesion Suite is a modular open-source toolkit for brain lesion image analysis that combines previously published preprocessing, segmentation, and evaluation components, though the paper reports no new quantitativ...

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