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

  • Tonghe Wang
  • Yang Lei
  • Haipeng Tang
  • Zhuo He
  • Richard Castillo
  • Cheng Wang
  • Dianfu Li
  • Kristin Higgins
  • Tian Liu
  • Walter J. Curran
  • Weihua ZhouEmail author
  • Xiaofeng YangEmail author
Original Article



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.


Myocardial perfusion SPECT segmentation machine learning 



Single-photon emission computed tomography


Myocardial perfusion SPECT


Left ventricular


Computed tomography


Magnetic resonance imaging


Binary cross entropy


Dice similarity coefficient


Ejection fraction


End-diastolic volume


End-systolic volume



This research is supported in part by the National Cancer Institute of the National Institutes of Health under Award Number R01CA215718 and Emory Winship Cancer Institute pilot Grant. This research is also supported by the American Heart Association under Award Number 17AIREA33700016.


Dr. Kristin Higgins is consulting for Astra Zeneca, Varian, on advisory board for Genentech, and receiving research funding from RefleXion Medical. Tonghe Wang, Yang Lei, Haipeng Tang, Zhuo He, Richard Castillo, Cheng Wang, Dianfu Li, Tian Liu, Walter J. Curran, Weihua Zhou, and Xiaofeng Yang declare that they have no conflict of interest.

Supplementary material

12350_2019_1594_MOESM1_ESM.pptx (1.8 mb)
Supplementary material 1 (PPTX 1888 kb)


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Copyright information

© American Society of Nuclear Cardiology 2019

Authors and Affiliations

  • Tonghe Wang
    • 1
  • Yang Lei
    • 1
  • Haipeng Tang
    • 2
  • Zhuo He
    • 2
  • Richard Castillo
    • 1
  • Cheng Wang
    • 3
  • Dianfu Li
    • 3
  • Kristin Higgins
    • 1
  • Tian Liu
    • 1
  • Walter J. Curran
    • 1
  • Weihua Zhou
    • 2
    Email author
  • Xiaofeng Yang
    • 1
    Email author
  1. 1.Department of Radiation Oncology and Winship Cancer InstituteEmory UniversityAtlantaUSA
  2. 2.School of ComputingUniversity of Southern MississippiLong BeachUSA
  3. 3.Department of CardiologyThe First Affiliated Hospital of Nanjing Medical UniversityNanjingChina

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