用随机掩码增强3D脑肿瘤MRI修复，效果超越历届冠军。
Robust 3D Brain MRI Inpainting with Random Masking Augmentation
- 基于U-Net的3D图像修复框架，结合随机掩码增强泛化能力。
- 验证集上SSIM达0.873，测试集上达到0.919，显著优于往年方案。
- 适合医学影像修复、模型鲁棒性提升的研究者参考。
ASNR-MICCAI BraTS-Inpainting挑战赛旨在缓解深度学习模型在脑肿瘤MRI定量分析中因数据偏差带来的限制。本文详述了我们在2025年挑战赛中的参赛方案，提出一种新型深度学习框架，用于合成3D扫描中的健康组织。核心方法是基于U-Net架构，训练其修复人为损坏区域，并引入随机掩码增强策略以提升泛化性能。定量评估显示，该方法在验证集上取得SSIM 0.873±0.004、PSNR 24.996±4.694、MSE 0.005±0.087；在最终线上测试集上表现更优，达到SSIM 0.919±0.088、PSNR 26.932±5.057、RMSE 0.052±0.026。该成果在官方排行榜上排名第一，超越2023及2024年优胜方案。
原文摘要 · Abstract (English)
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.
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