mlsanityData to 14 Oct 2026

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

arXiv 2305.08992 ↗First posted 15 May 2023eess.IV cs.CV cs.LGcited by 8

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Abstract (arXiv)

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.

Key facts. Cited by 8 papers in the map (data through 14 Oct 2026). Builds on 14 of its 24 references that are in the map. First posted on arXiv 15 May 2023.

Builds on

14 of 24 references.

References · 14

  1. 2016Image-to-Image Translation with Conditional Adversarial Networkscited by 4,527
  2. 2018Free-Form Image Inpainting with Gated Convolutioncited by 594
  3. 2018Generative Image Inpainting with Contextual Attentioncited by 580
  4. 2021The RSNA-ASNR-MICCAI BraTS 2021 Benchmark on Brain Tumor Segmentation and Radiogenomic Classificationcited by 518
  5. 2018Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challengecited by 511
  6. 2021Large Scale Image Completion via Co-Modulated Generative Adversarial Networkscited by 220
  7. 2018Why rankings of biomedical image analysis competitions should be interpreted with carecited by 91
  8. 2021Image Inpainting via Conditional Texture and Structure Dual Generationcited by 66
  9. 2019Methods and open-source toolkit for analyzing and visualizing challenge resultscited by 60
  10. 2021Are we using appropriate segmentation metrics? Identifying correlates of human expert perception for CNN training beyond rolling the DICE coefficientcited by 26
  11. 2021Learn-Morph-Infer: a new way of solving the inverse problem for brain tumor modelingcited by 19
  12. 2022Multitask Brain Tumor Inpainting with Diffusion Models: A Methodological Reportcited by 18
2 more
  1. 2022Approaching Peak Ground Truthcited by 8
  2. 2022Deep Quality Estimation: Creating Surrogate Models for Human Quality Ratingscited by 6

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 3; 2026: 5.

8 papers.

  1. 2025Post-Processing Methods for Improving Accuracy in MRI Inpaintingcited by 1
  2. 2025CADD: Context aware disease deviations via restoration of brain images using normative conditional diffusion modelscited by 0
  3. 2025Generating healthy counterfactuals with denoising diffusion bridge modelscited by 0
  4. 2026POWDR: Pathology-preserving Outpainting with Wavelet Diffusion for 3D MRIcited by 0
  5. 2026VS-DDPM: Efficient Low-Cost Diffusion Model for Medical Modality Translationcited by 0
  6. 2026Local Label-Informed Feature Transfer for Generating Ground-Truth Medical Images: A Comparison of GAN- and Diffusion-Based Approachescited by 0
  7. 2026CATCH: Counterfactual Anatomical Tissue Inpainting with Conditional Haar Diffusioncited by 0
  8. 2026Zero-Shot Brain MRI Inpainting with 2.5D Unconditional Flow Priorscited by 0