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

A Multi-Scale Spatial Transformer U-Net for Simultaneously Automatic Reorientation and Segmentation of 3D Nuclear Cardiac Images