{"ID":2836909,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.20202","arxiv_id":"2511.20202","title":"Robust 3D Brain MRI Inpainting with Random Masking Augmentation","abstract":"The ASNR-MICCAI BraTS-Inpainting Challenge was established to mitigate dataset biases that limit deep learning models in the quantitative analysis of brain tumor MRI. This paper details our submission to the 2025 challenge, a novel deep learning framework for synthesizing healthy tissue in 3D scans. The core of our method is a U-Net architecture trained to inpaint synthetically corrupted regions, enhanced with a random masking augmentation strategy to improve generalization. Quantitative evaluation confirmed the efficacy of our approach, yielding an SSIM of 0.873$\\pm$0.004, a PSNR of 24.996$\\pm$4.694, and an MSE of 0.005$\\pm$0.087 on the validation set. On the final online test set, our method achieved an SSIM of 0.919$\\pm$0.088, a PSNR of 26.932$\\pm$5.057, and an RMSE of 0.052$\\pm$0.026. This performance secured first place in the BraTS-Inpainting 2025 challenge and surpassed the winning solutions from the 2023 and 2024 competitions on the official leaderboard.","short_abstract":"The ASNR-MICCAI BraTS-Inpainting Challenge was established to mitigate dataset biases that limit deep learning models in the quantitative analysis of brain tumor MRI. This paper details our submission to the 2025 challenge, a novel deep learning framework for synthesizing healthy tissue in 3D scans. The core of our met...","url_abs":"https://arxiv.org/abs/2511.20202","url_pdf":"https://arxiv.org/pdf/2511.20202v1","authors":"[\"Juexin Zhang\",\"Ying Weng\",\"Ke Chen\"]","published":"2025-11-25T11:26:10Z","proceeding":"cs.CV","tasks":"[\"cs.CV\"]","methods":"[]","has_code":false}