Automated quality control for segmentation of myocardial perfusion SPECT

J Nucl Med. 2009 Sep;50(9):1418-26. doi: 10.2967/jnumed.108.061333. Epub 2009 Aug 18.


Left ventricular (LV) segmentation, including accurate assignment of LV contours, is essential for the quantitative assessment of myocardial perfusion SPECT (MPS). Two major types of segmentation failures are observed in clinical practices: incorrect LV shape determination and incorrect valve-plane (VP) positioning. We have developed a technique to automatically detect these failures for both nongated and gated studies.

Methods: A standard Cedars-Sinai perfusion SPECT (quantitative perfusion SPECT [QPS]) algorithm was applied to derive LV contours in 318 consecutive (99m)Tc-sestamibi rest/stress MPS studies consisting of stress/rest scans with or without attenuation correction and gated stress/rest images (1,903 scans total). Two numeric parameters, shape quality control (SQC) and valve-plane quality control, were derived to categorize the respective contour segmentation failures. The results were compared with the visual classification of automatic contour adequacy by 3 experienced observers.

Results: The overall success of automatic LV segmentation in the 1,903 scans ranged from 66% on nongated images (incorrect shape, 8%; incorrect VP, 26%) to 87% on gated images (incorrect shape, 3%; incorrect VP, 10%). The overall interobserver agreement for visual classification of automatic LV segmentation was 61% for nongated scans and 80% for gated images; the agreement between gray-scale and color-scale display for these scans was 86% and 91%, respectively. To improve the reliability of visual evaluation as a reference, the cases with intra- and interobserver discrepancies were excluded, and the remaining 1,277 datasets were considered (101 with incorrect LV shape and 102 with incorrect VP position). For the SQC, the receiver-operating-characteristic area under the curve (ROC-AUC) was 1.0 +/- 0.00 for the overall dataset, with an optimal sensitivity of 100% and a specificity of 98%. The ROC-AUC was 1.0 in all specific datasets. The algorithm was also able to detect the VP position errors: VP overshooting with ROC-AUC, 0.91 +/- 0.01; sensitivity, 100%; and specificity, 70%; and VP undershooting with ROC-AUC, 0.96 +/- 0.01; sensitivity, 100%; and specificity, 70%.

Conclusion: A new automated method for quality control of LV MPS contours has been developed and shows high accuracy for the detection of failures in LV segmentation with a variety of acquisition protocols. This technique may lead to an improvement in the objective, automated quantitative analysis of MPS.

Publication types

  • Evaluation Study
  • Research Support, N.I.H., Extramural

MeSH terms

  • Algorithms*
  • Artificial Intelligence
  • California
  • Female
  • Humans
  • Image Enhancement / methods
  • Image Enhancement / standards
  • Image Interpretation, Computer-Assisted / methods*
  • Image Interpretation, Computer-Assisted / standards
  • Male
  • Middle Aged
  • Pattern Recognition, Automated / methods*
  • Pattern Recognition, Automated / standards
  • Perfusion Imaging / methods*
  • Quality Assurance, Health Care / methods
  • Quality Control
  • Reproducibility of Results
  • Sensitivity and Specificity
  • Tomography, Emission-Computed, Single-Photon / methods*
  • Tomography, Emission-Computed, Single-Photon / standards
  • Ventricular Dysfunction, Left / diagnostic imaging*