Robust 3D Brain MRI Inpainting with Random Masking Augmentation
Juexin Zhang, Ying Weng, Ke Chen

TL;DR
This paper presents a novel 3D brain MRI inpainting method using a U-Net with random masking augmentation, achieving state-of-the-art results and winning the 2025 BraTS-Inpainting Challenge.
Contribution
The paper introduces a new deep learning framework with a random masking augmentation strategy for improved 3D MRI inpainting performance.
Findings
Achieved top performance in the 2025 BraTS-Inpainting Challenge.
Outperformed previous years' winning solutions.
Demonstrated high SSIM and PSNR scores on validation and test sets.
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 0.8730.004, a PSNR of 24.9964.694, and an MSE of 0.0050.087 on the validation set. On the final online test set, our method achieved an SSIM of 0.9190.088, a PSNR of 26.9325.057, and an RMSE of 0.0520.026. This performance secured first place in the BraTS-Inpainting 2025 challenge and surpassed the…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsGenerative Adversarial Networks and Image Synthesis · Advanced Neural Network Applications · Brain Tumor Detection and Classification
