A Two-Stage Cascade Model with Variational Autoencoders and Attention Gates for MRI Brain Tumor Segmentation
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Chenggang Lyu, Hai Shu
2020
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.
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