Accurate segmentation of the heart is essential for personalized blood flow
simulations and surgical intervention planning. A recent advancement in image
recognition is the Vision Transformer (ViT), which expands the field of view to
encompass a greater portion of the global image context. We adapted ViT for
three-dimensional volume inputs. Cardiac computed tomography (CT) volumes from
39 patients, featuring up to 20 timepoints representing the complete cardiac
cycle, were utilized. Our network incorporates a modified ResNet50 block as
well as a ViT block and employs cascade upsampling with skip connections.
Despite its increased model complexity, our hybrid Transformer-Residual U-Net
framework, termed TRUNet, converges in significantly less time than residual
U-Net while providing comparable or superior segmentations of the left
ventricle, left atrium, left atrial appendage, ascending aorta, and pulmonary
veins. TRUNet offers more precise vessel boundary segmentation and better
captures the heart's overall anatomical structure compared to residual U-Net,
as confirmed by the absence of extraneous clusters of missegmented voxels. In
terms of both performance and training speed, TRUNet exceeded U-Net, a commonly
used segmentation architecture, making it a promising tool for 3D semantic
segmentation tasks in medical imaging. The code for TRUNet is available at
github.com/ljollans/TRUNet.

心脏准确分割对于个性化血流模拟和手术干预规划至关重要。我们改进了 ViT 以适应三维体积输入，并将其与 ResNet50 和级联上采样相结合的方式构建了 TRUNet。TRUNet 在比传统的 Residual U-Net 更短的时间内收敛，并且能产生与其相媲美或更好的左心室、左心房、左心房附属物、肺动脉和升主动脉的分割效果。 TRUNet 能更准确地分割血管边界，并更好地捕捉到心脏的整体解剖结构，对于医学影像中的三维语义分割任务具有很大潜力。