TY - JOUR
T1 - Algorithm to quantify nuclear features and confidence intervals for classification of oral neoplasia from high-resolution optical images
AU - Yang, Eric C.
AU - Brenes, David R.
AU - Vohra, Imran S.
AU - Schwarz, Richard A.
AU - Williams, Michelle D.
AU - Vigneswaran, Nadarajah
AU - Gillenwater, Ann M.
AU - Richards-Kortum, Rebecca R.
N1 - Funding Information:
This work was supported by the National Institutes of Health (Grant No. RO1CA103830) (to R. Richards-Kortum), No. RO1CA185207 (to R. Richards-Kortum), No. RO1DE024392 (to N. Vigneswaran), No. F30CA213922 (to E. Yang); and by the Cancer Prevention and Research Institute of Texas (CPRIT) Grant No. RP100932 (to R. Richards-Kortum).
Publisher Copyright:
© The Authors.
PY - 2020/9/1
Y1 - 2020/9/1
N2 - Purpose: In vivo optical imaging technologies like high-resolution microendoscopy (HRME) can image nuclei of the oral epithelium. In principle, automated algorithms can then calculate nuclear features to distinguish neoplastic from benign tissue. However, images frequently contain regions without visible nuclei, due to biological and technical factors, decreasing the data available to and accuracy of image analysis algorithms. Approach: We developed the nuclear density-confidence interval (ND-CI) algorithm to determine if an HRME image contains sufficient nuclei for classification, or if a better image is required. The algorithm uses a convolutional neural network to exclude image regions without visible nuclei. Then the remaining regions are used to estimate a confidence interval (CI) for the number of abnormal nuclei per mm2, a feature used by a previously developed algorithm (called the ND algorithm), to classify images as benign or neoplastic. The range of the CI determines whether the ND-CI algorithm can classify an image with confidence, and if so, the predicted category. The ND and ND-CI algorithm were compared by calculating their positive predictive value (PPV) and negative predictive value (NPV) on 82 oral biopsies with histopathologically confirmed diagnoses. Results: After excluding the images that could not be classified with confidence, the ND-CI algorithm had higher PPV (65% versus 59%) and NPV (78% versus 75%) than the ND algorithm. Conclusions: The ND-CI algorithm could improve the real-time classification of HRME images of the oral epithelium by informing the user if an improved image is required for diagnosis.
AB - Purpose: In vivo optical imaging technologies like high-resolution microendoscopy (HRME) can image nuclei of the oral epithelium. In principle, automated algorithms can then calculate nuclear features to distinguish neoplastic from benign tissue. However, images frequently contain regions without visible nuclei, due to biological and technical factors, decreasing the data available to and accuracy of image analysis algorithms. Approach: We developed the nuclear density-confidence interval (ND-CI) algorithm to determine if an HRME image contains sufficient nuclei for classification, or if a better image is required. The algorithm uses a convolutional neural network to exclude image regions without visible nuclei. Then the remaining regions are used to estimate a confidence interval (CI) for the number of abnormal nuclei per mm2, a feature used by a previously developed algorithm (called the ND algorithm), to classify images as benign or neoplastic. The range of the CI determines whether the ND-CI algorithm can classify an image with confidence, and if so, the predicted category. The ND and ND-CI algorithm were compared by calculating their positive predictive value (PPV) and negative predictive value (NPV) on 82 oral biopsies with histopathologically confirmed diagnoses. Results: After excluding the images that could not be classified with confidence, the ND-CI algorithm had higher PPV (65% versus 59%) and NPV (78% versus 75%) than the ND algorithm. Conclusions: The ND-CI algorithm could improve the real-time classification of HRME images of the oral epithelium by informing the user if an improved image is required for diagnosis.
KW - Microscopy
KW - Oral cancer
KW - Parameter estimation
KW - Semantic segmentation
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U2 - 10.1117/1.JMI.7.5.054502
DO - 10.1117/1.JMI.7.5.054502
M3 - Article
C2 - 32999894
AN - SCOPUS:85096576979
SN - 2329-4302
VL - 7
JO - Journal of Medical Imaging
JF - Journal of Medical Imaging
IS - 5
M1 - 054502
ER -