  Current research on deep learning for medical image segmentation exposes
their limitations in learning either global semantic information or local
contextual information. To tackle these issues, a novel network named
SegTransVAE is proposed in this paper. SegTransVAE is built upon
encoder-decoder architecture, exploiting transformer with the variational
autoencoder (VAE) branch to the network to reconstruct the input images jointly
with segmentation. To the best of our knowledge, this is the first method
combining the success of CNN, transformer, and VAE. Evaluation on various
recently introduced datasets shows that SegTransVAE outperforms previous
methods in Dice Score and $95\%$-Haudorff Distance while having comparable
inference time to a simple CNN-based architecture network. The source code is
available at: https://github.com/itruonghai/SegTransVAE.
