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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

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

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-Andr\'e 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
This is my paper
classification eess.IV cs.CVcs.LG
keywords brainchallengealgorithmshealthyinpaintingbratsimagessegmentation
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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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 5 Pith papers

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

  1. VS-DDPM: Efficient Low-Cost Diffusion Model for Medical Modality Translation

    cs.CV 2026-04 unverdicted novelty 6.0

    VS-DDPM accelerates 3D diffusion models for medical modality translation, reaching SOTA Dice scores of 0.80-0.88 and SSIM 0.95 on missing MRI synthesis in BraTS2025 while remaining competitive on tumor removal and sCT tasks.

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

    cs.CV 2026-07 accept novelty 5.5

    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

    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. VS-DDPM: Efficient Low-Cost Diffusion Model for Medical Modality Translation

    cs.CV 2026-04 unverdicted novelty 5.0

    VS-DDPM accelerates 3D diffusion-based medical image translation, achieving SOTA Dice scores of 0.80-0.88 and SSIM 0.95 on missing MRI synthesis while remaining competitive on other modality tasks.

  5. 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.