Vision Transformers提升3D心脏CT多标签分割效率
Vision Transformers increase efficiency of 3D cardiac CT multi-label segmentation
AI总结：
本文提出融合ViT与改进ResNet50的3D TRUNet，用于全心脏多标签CT分割，在更短训练时间内取得优于3D U-Net和UNETR的精度。
摘要：
准确的心脏分割对于个性化血流模拟和手术干预规划至关重要。分割需要在每个空间维度上都准确，而逐切片分割数据无法保证这一点。研究使用了两个心脏计算机断层扫描（CT）数据集：一个包含来自39名患者、覆盖整个心动周期的760个容积，另一个包含来自60名患者的60个容积，用于训练网络以在3D中同时分割代表整个心脏的多个区域。分割区域包括左心房和右心房、左心室和右心室、左心室心肌、升主动脉、肺动脉、肺静脉以及左心耳。研究将广泛使用的3D U-Net和UNETR架构与本文提出的、针对大尺寸容积输入优化的方法进行了比较。所提出的网络架构称为Transformer Residual U-Net（TRUNet），保留了U-Net的级联下采样编码器、级联上采样解码器和跳跃连接，同时在编码器中加入Vision Transformer（ViT）模块以及改进的ResNet50模块。TRUNet在所有结构上都取得了更高的分割性能，而训练时间约为3D U-Net和UNETR所需时间的一半。与其他方法相比，所提出的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。快速的训练时间和对相邻结构的准确描绘使TRUNet成为医学图像分割任务中很有前景的候选方案。TRUNet的代码可在github.com/ljollans/TRUNet获取。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
