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Improved Detection of Chronic Obstructive Pulmonary Disease at Chest CT Using the Mean Curvature of Isophotes
Peter Savadjiev1, Benoit Gallix1, Morteza Rezanejad1
1Department of Diagnostic Radiology (P.S., S.B., A.S., R.F., C.R.), Research Institute (R.F., C.R., D.H.E., R.J.D.), Meakins-Christie Laboratories, Research Institute (D.H.E., R.J.D.), Centre for Innovative Medicine, Research Institute (R.J.D.), and Montreal Chest Institute (R.J.D.), McGill University Health Centre, 1001 Décarie Blvd, Montréal, QC, Canada H4A 3J1; Department of Pathology (P.S.), Medical Physics Unit, Department of Oncology (P.S.), School of Computer Science (P.S., K.S.), Department of Epidemiology, Biostatistics and Occupational Health (S.B.), Segal Cancer Centre and Lady Davis Institute for Medical Research, Jewish General Hospital (R.F.), and Department of Medicine (D.H.E., R.J.D.), McGill University, Montreal, Quebec, Canada; Institut de Chirurgie Guidée par l'Image, IHU Strasbourg, Strasbourg, France (B.G.); Bernhardt-Walther Laboratory, Department of Psychology, University of Toronto, Toronto, Ontario, Canada (M.R.); and Lakeshore General Hospital, Pointe-Claire, Quebec, Canada (R.J.D.).
Purpose:
To determine if the mean curvature of isophotes (MCI), a standard computer vision technique, can be used to improve detection of chronic obstructive pulmonary disease (COPD) at chest CT.
Materials And Methods:
In this retrospective study, chest CT scans were obtained in 243 patients with COPD and 31 controls (among all 274: 151 women [mean age, 70 years; range, 44-90 years] and 123 men [mean age, 71 years; range, 29-90 years]) from two community practices between 2006 and 2019. A convolutional neural network (CNN) architecture was trained on either CT images or CT images transformed through the MCI algorithm. Separately, a linear classification based on a single feature derived from the MCI computation (called hMCI1) was also evaluated. All three models were evaluated with cross-validation, using precision-macro and recall-macro metrics, that is, the mean of per-class precision and recall values, respectively (the latter being equivalent to balanced accuracy).
Results:
Linear classification based on hMCI1 resulted in a higher recall-macro relative to the CNN trained and applied on CT images (0.85 [95% CI: 0.84, 0.86] vs 0.77 [95% CI: 0.75, 0.79]) but with a similar reduction in precision-macro (0.66 [95% CI: 0.65, 0.67] vs 0.77 [95% CI: 0.75, 0.79]). The CNN model trained and applied on MCI-transformed images had a higher recall-macro (0.85 [95% CI: 0.83, 0.87] vs 0.77 [95% CI: 0.75, 0.79]) and precision-macro (0.85 [95% CI: 0.83, 0.87] vs 0.77 [95% CI: 0.75, 0.79]) relative to the CNN trained and applied on CT images.
Conclusion:
The MCI algorithm may be valuable toward the automated detection and diagnosis of COPD on chest CT scans as part of a CNN-based pipeline or with stand-alone features.Keywords: Chronic Obstructive Pulmonary Disease, Quantification, Lung, CT Supplemental material is available for this article. See also the invited commentary by Vannier in this issue.© RSNA, 2021.
