ArticleJournal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology2020
A learning-based automatic segmentation and quantification method on left ventricle in gated myocardial perfusion SPECT imaging: A feasibility study.
Article in Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers.
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38 citing papers in PubMed, 100 citations in OpenAlex.
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- Deep learning, another important tool for improving acquisition efficiency in SPECT MPI Imaging.Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology · 2021Article
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Authors and funding
12 authors at 3 institutions in 2 countries.
Funding
Abstract
backgroundThe 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.
methodsWe 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.
resultsThe 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%.
conclusionThese 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.
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