  Recent rising interests in patient-specific thoracic surgical planning and
simulation require efficient and robust creation of digital anatomical models
from automatic medical image segmentation algorithms. Deep learning (DL) is now
state-of-the-art in various radiological tasks, and U-shaped DL models have
particularly excelled in medical image segmentation since the inception of the
2D UNet. To date, many variants of U-shaped models have been proposed by the
integration of different attention mechanisms and network configurations.
Systematic benchmark studies which analyze the architecture of these models by
leveraging the recent development of the multi-label databases, can provide
valuable insights for clinical deployment and future model designs, but such
studies are still rare. We conduct the first systematic benchmark study for
variants of 3D U-shaped models (3DUNet, STUNet, AttentionUNet, SwinUNETR,
FocalSegNet, and a novel 3D SwinUnet with four variants) with a focus on
CT-based anatomical segmentation for thoracic surgery. Our study systematically
examines the impact of different attention mechanisms, the number of resolution
stages, and network configurations on segmentation accuracy and computational
complexity. To allow cross-reference with other recent benchmarking studies, we
also included a performance assessment of the BTCV abdominal structural
segmentation. With the STUNet ranking at the top, our study demonstrated the
value of CNN-based U-shaped models for the investigated tasks and the benefit
of residual blocks in network configuration designs to boost segmentation
performance.
