Automatic Valve Plane Localization in Myocardial Perfusion SPECT/CT by Machine Learning: Anatomic and Clinical Validation.
Precise definition of the mitral valve plane (VP) during segmentation of the left ventricle for SPECT myocardial perfusion imaging (MPI) quantification often requires manual adjustment, which affects the quantification of perfusion. We developed a machine learning approach using support vector machines (SVM) for automatic VP placement.Methods:A total of 392 consecutive patients undergoing99mTc-tetrofosmin stress (5 min; mean ± SD, 350 ± 54 MBq) and rest (5 min; 1,024 ± 153 MBq) fast SPECT MPI attenuation corrected (AC) by CT and same-day coronary CT angiography were studied; included in the 392 patients were 48 patients who underwent invasive coronary angiography and had no known coronary artery disease. The left ventricle was segmented with standard clinical software (quantitative perfusion SPECT) by 2 experts, adjusting the VP if needed. Two-class SVM models were computed from the expert placements with 10-fold cross validation to separate the patients used for training and those used for validation. SVM probability estimates were used to compute the best VP position. Automatic VP localizations on AC and non-AC images were compared with expert placement on coronary CT angiography. Stress and rest total perfusion deficits and detection of per-vessel obstructive stenosis by invasive coronary angiography were also compared.Results:Bland-Altman 95% confidence intervals (CIs) for VP localization by SVM and experts for AC stress images (bias, 1; 95% CI, -5 to 7 mm) and AC rest images (bias, 1; 95% CI, -7 to 10 mm) were narrower than interexpert 95% CIs for AC stress images (bias, 0; 95% CI, -8 to 8 mm) and AC rest images (bias, 0; 95% CI, -10 to 10 mm) (P< 0.01). Bland-Altman 95% CIs for VP localization by SVM and experts for non-AC stress images (bias, 1; 95% CI, -4 to 6 mm) and non-AC rest images (bias, 2; 95% CI, -7 to 10 mm) were similar to interexpert 95% CIs for non-AC stress images (bias, 0; 95% CI, -6 to 5 mm) and non-AC rest images (bias, -1; 95% CI, -9 to 7 mm) (Pwas not significant [NS]). For regional detection of obstructive stenosis, ischemic total perfusion deficit areas under the receiver operating characteristic curve for the 2 experts (AUC, 0.79 [95% CI, 0.7-0.87]; AUC, 0.81 [95% CI, 0.73-0.89]) and the SVM (0.82 [0.74-0.9]) for AC data were the same (P= NS) and were higher than those for the unadjusted VP (0.63 [0.53-0.73]) (P< 0.01). Similarly, for non-AC data, areas under the receiver operating characteristic curve for the experts (AUC, 0.77 [95% CI, 0.69-0.89]; AUC, 0.8 [95% CI, 0.72-0.88]) and the SVM (0.79 [0.71-0.87]) were the same (P= NS) and were higher than those for the unadjusted VP (0.65 [0.56-0.75]) (P< 0.01).Conclusion:Machine learning with SVM allows automatic and accurate VP localization, decreasing user dependence in SPECT MPI quantification.
Department of Imaging, Medicine, and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California.
Julian
J
Betancur
Department of Imaging, Medicine, and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California.
Mathieu
M
Rubeaux
Cardiac Imaging, Department of Nuclear Medicine, University Hospital Zurich, Zurich, Switzerland; and.
Tobias A
TA
Fuchs
Department of Imaging, Medicine, and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California.
Yuka
Y
Otaki
Department of Imaging, Medicine, and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California.
Yoav
Y
Arnson
Department of Imaging, Medicine, and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California.
Leandro
L
Slipczuk
Cardiac Imaging, Department of Nuclear Medicine, University Hospital Zurich, Zurich, Switzerland; and.
Dominik C
DC
Benz
Department of Imaging, Medicine, and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California.
Guido
G
Germano
Department of Imaging, Medicine, and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California.
Damini
D
Dey
Department of Computer Science, National Taiwan University, Taipei, Taiwan.
Chih-Jen
CJ
Lin
Department of Imaging, Medicine, and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California.
Daniel S
DS
Berman
Cardiac Imaging, Department of Nuclear Medicine, University Hospital Zurich, Zurich, Switzerland; and.
Philipp A
PA
Kaufmann
Department of Imaging, Medicine, and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California Piotr.Slomka@cshs.org.
Piotr J
PJ
Slomka
D059925:Anatomic Landmarks / Q000000981:diagnostic imaging* / Q000473:pathology; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods; D000069550:Machine Learning; D008875:Middle Aged; D008943:Mitral Valve / Q000000981:diagnostic imaging* / Q000473:pathology; D055414:Myocardial Perfusion Imaging / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D000072098:Single Photon Emission Computed Tomography Computed Tomography / Q000379:methods*
SPECT quantification; coronary CT angiography; machine learning; myocardial perfusion imaging; support vector classification