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.
Leveraging the recent development of large multi-label databases, systematic
benchmark studies for these models can provide valuable insights for clinical
deployment and future model designs, but such studies are still rare. We
conduct the first 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, 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.

最近对患者特定胸部手术规划和仿真的兴趣日益增加，要求从自动医学影像分割算法中高效、稳健地创建数字解剖模型。深度学习（DL）现在在各种放射学任务中处于领先地位，U 型 DL 模型在医学影像分割方面表现出色，自 2D UNet 的诞生以来已经有很多 U 型模型的变体。目前，尚缺乏对这些模型进行系统的基准研究，尽管最近大规模多标签数据库的发展为该领域提供了机会。我们首次对 3D U 型模型的变体（3DUNet，STUNet，AttentionUNet，SwinUNETR，FocalSegNet 以及一种新的带有四种变体的 3D SwinUnet）进行了基准研究，其重点是基于 CT 的胸部手术解剖分割。我们的研究系统地考察了不同注意力机制、分辨率级别数量和网络配置对分割准确率和计算复杂度的影响，并进行了与最近的其他基准研究的交叉参考，包括对 BTCV 腹部结构分割的性能评估。在 STUNet 排名首位的情况下，我们的研究证明了用于所研究任务的基于 CNN 的 U 型模型的价值，以及在网络配置设计中使用剩余块以提高分割性能的好处。