用U-Net修复受损脑部MRI，恢复健康组织细节。
U-Net Based Healthy 3D Brain Tissue Inpainting
- 基于U-Net结构，通过随机遮蔽训练增强泛化能力。
- 在BraTS数据集上达到SSIM 0.841、PSNR 23.257、MSE 0.007。
- 结果稳定可靠，获挑战赛第一名，适合医学图像修复应用。
本文提出一种新方法，从掩码输入图像中合成健康的3D脑组织，聚焦于'ASNR-MICCAI BraTS Local Synthesis of Tissue via Inpainting'任务。所提方法采用U-Net架构，有效重建脑部MRI扫描中缺失或损坏区域。为提升模型泛化能力与鲁棒性，训练时引入随机遮蔽健康图像的数据增强策略。模型在BraTS-Local-Inpainting数据集上训练，表现优异。评估指标包括结构相似性（SSIM）、峰值信噪比（PSNR）和均方误差（MSE），在验证集上分别取得SSIM 0.841、PSNR 23.257、MSE 0.007的成果。各指标标准差较低，分别为SSIM 0.103、PSNR 4.213、MSE 0.007，表明模型在多种输入场景下具有高可靠性与一致性。该方法在挑战赛中获得第一名。
原文摘要 · Abstract (English)
This paper introduces a novel approach to synthesize healthy 3D brain tissue from masked input images, specifically focusing on the task of 'ASNR-MICCAI BraTS Local Synthesis of Tissue via Inpainting'. Our proposed method employs a U-Net-based architecture, which is designed to effectively reconstruct the missing or corrupted regions of brain MRI scans. To enhance our model's generalization capabilities and robustness, we implement a comprehensive data augmentation strategy that involves randomly masking healthy images during training. Our model is trained on the BraTS-Local-Inpainting dataset and demonstrates the exceptional performance in recovering healthy brain tissue. The evaluation metrics employed, including Structural Similarity Index (SSIM), Peak Signal-to-Noise Ratio (PSNR), and Mean Squared Error (MSE), consistently yields impressive results. On the BraTS-Local-Inpainting validation set, our model achieved an SSIM score of 0.841, a PSNR score of 23.257, and an MSE score of 0.007. Notably, these evaluation metrics exhibit relatively low standard deviations, i.e., 0.103 for SSIM score, 4.213 for PSNR score and 0.007 for MSE score, which indicates that our model's reliability and consistency across various input scenarios. Our method also secured first place in the challenge.
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