Single-Photon Emission Computed Tomography (SPECT) left ventricular assessment protocols are important for detecting ischemia in high-risk patients. To quantitatively measure myocardial function, clinicians depend on commercially available solutions to segment and reorient the left ventricle (LV) for evaluation. Based on large normal datasets, the segmentation performance and the high price of these solutions can hinder the availability of reliable and precise localization of the LV delineation. To overcome the aforementioned shortcomings this paper aims to give a recipe for diagnostic centers as well as for clinics to automatically segment the myocardium based on small and low-quality labels on reconstructed SPECT, complete field-of-view (FOV) volumes. A combination of Continuous Max-Flow (CMF) with prior shape information is developed to augment the 3D U-Net self-supervised learning (SSL) approach on various geometries of SPECT apparatus. Experimental results on the acquired dataset have shown a 5-10\% increase in quantitative metrics based on the previous State-of-the-Art (SOTA) solutions, suggesting a good plausible way to tackle the few-shot SSL problem on high-noise SPECT cardiac datasets.

翻译：单光子发射计算机断层扫描（SPECT）左心室评估方案对于检测高风险患者的缺血状态具有重要意义。为定量评估心肌功能，临床医师依赖商业解决方案进行左心室的图像分割与重定向。基于大规模正常数据集时，这些解决方案的分割性能与高昂价格可能制约左心室轮廓定位的可靠性与精确性。为克服上述局限，本文旨在为诊断中心及临床机构提供一种基于SPECT全视场重建体积中低质量小样本标签实现心肌自动分割的方案。该方法结合连续最大流（CMF）与先验形状信息，针对不同几何构型的SPECT设备增强3D U-Net自监督学习（SSL）性能。实验结果表明，与现有最先进（SOTA）解决方案相比，所提方法在采集数据集上的量化指标提升5-10%，为解决高噪声SPECT心脏数据的小样本SSL问题提供了有效途径。