a learning-based automatic segmentation and quantification method on left ventricle in gated myocardial perfusion spect imaging: a feasibility study

The performance of left ventricular (lv) functional assessment using gated myocardial perfusion spect (mps) relies on the accuracy of segmentation. current methods require manual adjustments that are tedious and subjective. we propose a novel machine-learning-based method to automatically segment lv myocardium and measure its volume in gated mps imaging without human intervention. we used an end-to-end fully convolutional neural network to segment lv myocardium by delineating its endocardial and epicardial surface. a novel compound loss function, which encourages similarity and penalizes discrepancy between prediction and training dataset, is utilized in training stage to achieve excellent performance. we retrospectively investigated 32 normal patients and 24 abnormal patients, whose lv myocardial contours automatically segmented by our method were compared with those delineated by physicians as the ground truth. the results of our method demonstrated very good agreement with the ground truth. the average dsc metrics and hausdorff distance of the contours delineated by our method are larger than 0.900 and less than 1 cm, respectively, among all 32 + 24 patients of all phases. the correlation coefficient of the lv myocardium volume between ground truth and our results is 0.910 ± 0.061 (p < 0.001), and the mean relative error of lv myocardium volume is − 1.09 ± 3.66%. these results strongly indicate the feasibility of our method in accurately quantifying lv myocardium volume change over the cardiac cycle. the learning-based segmentation method in gated mps imaging has great promise for clinical use.