Representative images of normal and high-grade CIN images with epithelium segmentation results. White curves and white arrows indicate the probe-tissue interface segmented by the CADx algorithm and black curves and black arrows indicate the segmented boundary between the epithelium and stroma. [(a)–(d)] Typical normal cervical OCT images and segmentation results. All structures are well-defined, including the epithelium (EP), the basement membrane (BM), and the stroma (ST). [(e)–(l)] CIN 2 and CIN 3 OCT images and the detections. The layered architecture becomes irregular or is not apparent. Images shown in (e) and (f) and (i) and (j) were correctly classified. Images shown in (g) and (h) and (k) and (l) were misclassified.
The flow chart of the CAD algorithm. ANOVA: Analysis of variance. PCA: Principal component analysis. LDA: Linear discriminant analysis.
Box plots of the four quantified image features. On each box, the central mark is the median, the edges of the box are the 25th and 75th percentiles, the whiskers extend to the most extreme data points not considered outliers, and the outliers are marked by the plus sign. (a) The thickness. (b) The standard deviation (Std) of thickness within an image. (c) The contrast. (d) The standard deviation of contrast within an image. The p-values are from F-tests with the null hypothesis that the means have no difference.
The ROC curve generated by varying the prior probability in the LDA classification. The arrow indicates the true positive rate and false positive rate with the empirical prior probability estimated from the image set.
(a) CIN 1 and (b) inflammation image that was misclassified as high-grade CIN images. The layered structure in these two images was far less visible compared to normal images. (c) CIN 3 image misclassified as normal/inflammation/CIN 1. (d) The active contour segmentation of (c) failed due to irregular probe-tissue contact.
LDA classification results compared to histopathology.
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