Accurate reorientation and segmentation of the left ventricular (LV) is essential for the quantitative analysis of myocardial perfusion imaging (MPI), in which one critical step is to reorient the reconstructed transaxial nuclear cardiac images into standard short-axis slices for subsequent image processing. Small-scale LV myocardium (LV-MY) region detection and the diverse cardiac structures of individual patients pose challenges to LV segmentation operation. To mitigate these issues, we propose an end-to-end model, named as multi-scale spatial transformer UNet (MS-ST-UNet), that involves the multi-scale spatial transformer network (MSSTN) and multi-scale UNet (MSUNet) modules to perform simultaneous reorientation and segmentation of LV region from nuclear cardiac images. The proposed method is trained and tested using two different nuclear cardiac image modalities: 13N-ammonia PET and 99mTc-sestamibi SPECT. We use a multi-scale strategy to generate and extract image features with different scales. Our experimental results demonstrate that the proposed method significantly improves the reorientation and segmentation performance. This joint learning framework promotes mutual enhancement between reorientation and segmentation tasks, leading to cutting edge performance and an efficient image processing workflow. The proposed end-to-end deep network has the potential to reduce the burden of manual delineation for cardiac images, thereby providing multimodal quantitative analysis assistance for physicists.

翻译：左心室（LV）的精确重定向与分割对于心肌灌注成像（MPI）的定量分析至关重要，其中关键步骤是将重建的横断面核心脏图像重定向为标准短轴切片，以进行后续图像处理。小尺度左心室心肌（LV-MY）区域检测以及个体患者心脏结构的多样性给左心室分割操作带来了挑战。为解决这些问题，我们提出了一种端到端模型——多尺度空间变换UNet（MS-ST-UNet），该模型包含多尺度空间变换网络（MSSTN）和多尺度UNet（MSUNet）模块，可同时对核心脏图像中的左心室区域进行重定向与分割。所提出的方法使用两种不同的核心脏图像模态进行训练和测试：13N-氨PET和99mTc-甲氧基异丁基异腈SPECT。我们采用多尺度策略生成并提取不同尺度的图像特征。实验结果表明，所提出的方法显著提升了重定向与分割性能。这种联合学习框架促进了重定向与分割任务之间的相互增强，实现了前沿性能与高效的图像处理流程。该端到端深度网络有望减轻心脏图像手动标注的负担，从而为物理学家提供多模态定量分析辅助。