Accurate segmentation of the heart is essential for personalized blood flow simulations and surgical intervention planning. Segmentations need to be accurate in every spatial dimension, which is not ensured by segmenting data slice by slice. Two cardiac computed tomography (CT) datasets consisting of 760 volumes across the whole cardiac cycle from 39 patients, and of 60 volumes from 60 patients respectively were used to train networks to simultaneously segment multiple regions representing the whole heart in 3D. The segmented regions included the left and right atrium and ventricle, left ventricular myocardium, ascending aorta, pulmonary arteries, pulmonary veins, and left atrial appendage. The widely used 3D U-Net and the UNETR architecture were compared to our proposed method optimized for large volumetric inputs. The proposed network architecture, termed Transformer Residual U-Net (TRUNet), maintains the cascade downsampling encoder, cascade upsampling decoder and skip connections from U-Net, while incorporating a Vision Transformer (ViT) block in the encoder alongside a modified ResNet50 block. TRUNet reached higher segmentation performance for all structures within approximately half the training time needed for 3D U-Net and UNETR. The proposed method achieved more precise vessel boundary segmentations and better captured the heart's overall anatomical structure compared to the other methods. The fast training time and accurate delineation of adjacent structures makes TRUNet a promising candidate for medical image segmentation tasks. The code for TRUNet is available at github.com/ljollans/TRUNet.

翻译：精确的心脏分割对于个性化血流模拟和手术规划至关重要。分割需要在每个空间维度上都保持准确性，而逐层分割数据无法保证这一点。本研究使用两个心脏计算机断层扫描（CT）数据集训练网络，以同时分割代表完整心脏三维结构的多个区域：一个数据集包含来自39名患者、覆盖整个心动周期的760个体积数据，另一个包含来自60名患者的60个体积数据。分割区域包括左心房、右心房、左心室、右心室、左心室心肌、升主动脉、肺动脉、肺静脉及左心耳。我们将广泛使用的3D U-Net和UNETR架构与我们针对大体积输入优化的方法进行了比较。所提出的网络架构称为Transformer残差U-Net（TRUNet），它在保留U-Net级联下采样编码器、级联上采样解码器和跳跃连接的同时，在编码器中集成了视觉Transformer（ViT）模块和改良的ResNet50模块。TRUNet在所有结构的分割性能上均达到更高水平，且训练时间仅为3D U-Net和UNETR所需时间的一半左右。与其他方法相比，所提方法实现了更精确的血管边界分割，并更好地捕捉了心脏的整体解剖结构。快速的训练时间和相邻结构的精准描绘使TRUNet成为医学图像分割任务的理想候选方法。TRUNet代码已发布于github.com/ljollans/TRUNet。