A new method incorporating deep learning with shape priors for left ventricular segmentation in myocardial perfusion SPECT images - Zhu, Fubao; Li, Longxi; Zhao, Jinyu; Zhao, Chen; Tang, Shaojie; Nan, Jiaofen; Li, Yanting; Zhao, Zhongqiang; Shi, Jianzhou; Chen, Zenghong; Han, Chuang; Jiang, Zhixin; et al. (Computers in biology and medicine, 2023)
Keywords: Accuracy; Algorithms; Cardiovascular disease; Cicatrix; Contours; Correlation coefficients; Datasets; Deep Learning; Dynamic programming; Epicardium; Heart; Heart Ventricles - diagnostic imaging; Heart Ventricles - pathology; Humans; Image processing; Image segmentation; Internal Medicine; Ischemia; Left ventricle; Myocardial perfusion SPECT; Myocardium; Other; Performance evaluation; Perfusion; Quantitative analysis; Retrospective Studies; Segmentation; Single photon emission computed tomography; Software; Stroke Volume; Tomography, Emission-Computed, Single-Photon - methods; Training; V-Net; Ventricle; Ventricular Function, Left; CNN; Segmentation; CAD; Myocardial perfusion SPECT; LVEF; ESV; LV; DP; STN; ROI; Left ventricle; Deep learning; MPS; DSC; EDV; V-Net; DP-ST-V-Net; HD; coronary artery disease; left ventricular ejection fraction; end-diastolic volume; spatial transform network; convolutional neural network; dynamic programming; end-systolic volume; shape-transformation VNet with dynamic programming; Hausdorff distance; region of interest; Dice similarity coefficient