A Two-Stage Cascade Model with Variational Autoencoders and Attention Gates for MRI Brain Tumor Segmentation

TL;DR

This paper introduces a two-stage encoder-decoder model with variational autoencoders and attention gates for improved MRI brain tumor segmentation, achieving high accuracy on BraTS 2020 datasets.

Contribution

It presents a novel two-stage segmentation framework utilizing variational autoencoders and attention mechanisms, enhancing accuracy and robustness over existing methods.

Findings

Achieved mean Dice scores of 0.9041, 0.8350, 0.7958 on BraTS 2020 validation set.

Attained Hausdorff distances of 4.953, 6.299, 23.608 for different tumor regions.

Code is publicly available for reproducibility and further research.

Abstract

Automatic MRI brain tumor segmentation is of vital importance for the disease diagnosis, monitoring, and treatment planning. In this paper, we propose a two-stage encoder-decoder based model for brain tumor subregional segmentation. Variational autoencoder regularization is utilized in both stages to prevent the overfitting issue. The second-stage network adopts attention gates and is trained additionally using an expanded dataset formed by the first-stage outputs. On the BraTS 2020 validation dataset, the proposed method achieves the mean Dice score of 0.9041, 0.8350, and 0.7958, and Hausdorff distance (95%) of 4.953, 6.299, and 23.608 for the whole tumor, tumor core, and enhancing tumor, respectively. The corresponding results on the BraTS 2020 testing dataset are 0.8729, 0.8357, and 0.8205 for Dice score, and 11.4288, 19.9690, and 15.6711 for Hausdorff distance. The code is publicly available at https://github.com/shu-hai/two-stage-VAE-Attention-gate-BraTS2020.