Abstract
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.
Abstract (translated)
本文提出了一种新颖的方法，用于从带有遮罩的输入图像中合成健康的人脑组织，特别关注的是“ASNR-MICCAI BraTS 局部组织插值”任务。我们提出的模型采用基于U-Net架构的设计，旨在有效重建大脑MRI扫描中缺失或损坏的部分区域。为了增强模型的泛化能力和鲁棒性，我们在训练过程中实施了一种全面的数据增强策略，即在随机遮罩健康图像时进行训练。 我们的模型是在BraTS-Local-Inpainting数据集上进行训练的，并且在恢复健康的脑组织方面表现出色。评估指标包括结构相似度指数（SSIM）、峰值信噪比（PSNR）和均方误差（MSE），这些指标持续显示出色的结果。在BraTS-Local-Inpainting验证集中，我们的模型达到了0.841的SSIM分数、23.257的PSNR分数以及0.007的MSE分数。 值得注意的是，这些评估指标的标准偏差相对较低，即SSIM得分为0.103，PSNR得分为4.213，MSE得分为0.007，这表明我们的模型在各种输入场景中的可靠性和一致性。此外，我们的方法在这次挑战赛中获得了第一名的成绩。
URL
https://arxiv.org/abs/2507.18126
