  The success of Transformer in computer vision has attracted increasing
attention in the medical imaging community. Especially for medical image
segmentation, many excellent hybrid architectures based on convolutional neural
networks (CNNs) and Transformer have been presented and achieve impressive
performance. However, most of these methods, which embed modular Transformer
into CNNs, struggle to reach their full potential. In this paper, we propose a
novel hybrid architecture for medical image segmentation called PHTrans, which
parallelly hybridizes Transformer and CNN in main building blocks to produce
hierarchical representations from global and local features and adaptively
aggregate them, aiming to fully exploit their strengths to obtain better
segmentation performance. Specifically, PHTrans follows the U-shaped
encoder-decoder design and introduces the parallel hybird module in deep
stages, where convolution blocks and the modified 3D Swin Transformer learn
local features and global dependencies separately, then a sequence-to-volume
operation unifies the dimensions of the outputs to achieve feature aggregation.
Extensive experimental results on both Multi-Atlas Labeling Beyond the Cranial
Vault and Automated Cardiac Diagnosis Challeng datasets corroborate its
effectiveness, consistently outperforming state-of-the-art methods. The code is
available at: https://github.com/lseventeen/PHTrans.
