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.

我们提出了一种名为多尺度空间变换 UNet（MS-ST-UNet）的端到端模型，用于从核心心脏图像中同时重新定位和分割左室（LV）区域。该方法采用多尺度策略生成和提取具有不同尺度的图像特征，并通过联合学习促进重新定位和分割任务之间的互相增强。该方法显著改进了重新定位和分割性能，能够为物理学家提供多模态定量分析辅助，减少手动描绘心脏图像的负担，并提供高效的图像处理工作流程。