U-Net Based Healthy 3D Brain Tissue Inpainting

TL;DR

This paper presents a U-Net based method for 3D brain tissue inpainting that effectively reconstructs healthy tissue from masked MRI scans, demonstrating high accuracy and robustness on the BraTS dataset.

Contribution

The study introduces a novel U-Net architecture combined with data augmentation for improved brain tissue inpainting, achieving state-of-the-art results and winning a challenge.

Findings

Achieved SSIM of 0.841, PSNR of 23.257, MSE of 0.007 on validation set.

Model demonstrates high reliability with low standard deviations in metrics.

Secured first place in the challenge.

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.