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.

翻译：随着患者特异性胸腔手术规划与模拟需求日益增长，亟需通过自动化医学图像分割算法高效可靠地构建数字化解剖模型。深度学习（DL）现已成为各类放射学任务的最新前沿技术，其中U型深度学习模型自二维UNet问世以来在医学图像分割领域表现尤为突出。至今，研究者通过集成不同注意力机制与网络结构提出了多种U型模型变体。借助近年来大规模多标签数据库的发展，对这些模型进行系统性基准研究可为临床部署及未来模型设计提供重要参考，然而此类研究仍较为稀缺。我们首次针对三维U型模型变体（包括3DUNet、STUNet、AttentionUNet、SwinUNETR、FocalSegNet及四种新型3D SwinUnet变体）开展基准研究，重点聚焦基于CT的胸腔手术解剖分割。本研究系统考察了不同注意力机制、分辨率阶段数量及网络结构对分割精度与计算复杂度的综合影响。为便于与其他近期基准研究进行交叉对比，我们还纳入了BTCV腹部结构分割的性能评估。研究表明，STUNet在各项任务中表现最优，验证了基于CNN的U型模型在目标任务中的价值，并揭示了残差模块在提升分割性能的网络结构设计中的重要作用。