Development of Algorithms for Automated Detection of Cervical Pre-Cancers With a Low-Cost, Point-of-Care, Pocket Colposcope

Goal: In this paper, we propose methods for (1) automatic feature extraction and classification for acetic acid and Lugol's iodine cervigrams and (2) methods for combining features/diagnosis of different contrasts in cervigrams for improved performance. Methods: We developed algorithms to pre-p...

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Vydané v:	IEEE transactions on biomedical engineering Ročník 66; číslo 8; s. 2306 - 2318
Hlavní autori:	Asiedu, Mercy Nyamewaa, Simhal, Anish, Chaudhary, Usamah, Mueller, Jenna L., Lam, Christopher T., Schmitt, John W., Venegas, Gino, Sapiro, Guillermo, Ramanujam, Nimmi
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	United States IEEE 01.08.2019 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Acetic acid Algorithms Automation Benign Cervix Cervix Uteri - diagnostic imaging Color Colposcopes colposcopy Computer-aided detection and diagnosis Diagnosis Early Detection of Cancer - instrumentation Early Detection of Cancer - methods Feature extraction Female global health Handheld computers Humans image acquisition Image Interpretation, Computer-Assisted - instrumentation Image Interpretation, Computer-Assisted - methods Inspection Iodine Lugol's iodine Machine Learning Pathology Phase shift keying Physicians Point-of-Care Systems Precancerous Conditions - diagnostic imaging predictive models segmentation Sensitivity Support vector machines Uterine Cervical Neoplasms - diagnostic imaging
ISSN:	0018-9294, 1558-2531, 1558-2531
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Abstract	Goal: In this paper, we propose methods for (1) automatic feature extraction and classification for acetic acid and Lugol's iodine cervigrams and (2) methods for combining features/diagnosis of different contrasts in cervigrams for improved performance. Methods: We developed algorithms to pre-process pathology-labeled cervigrams and extract simple but powerful color and textural-based features. The features were used to train a support vector machine model to classify cervigrams based on corresponding pathology for visual inspection with acetic acid, visual inspection with Lugol's iodine, and a combination of the two contrasts. Results: The proposed framework achieved a sensitivity, specificity, and accuracy of 81.3%, 78.6%, and 80.0%, respectively, when used to distinguish cervical intraepithelial neoplasia (CIN+) relative to normal and benign tissues. This is superior to the average values achieved by three expert physicians on the same data set for discriminating normal/benign cases from CIN+ (77% sensitivity, 51% specificity, and 63% accuracy). Conclusion: The results suggest that utilizing simple color- and textural-based features from visual inspection with acetic acid and visual inspection with Lugol's iodine images may provide unbiased automation of cervigrams. Significance: This would enable automated, expert-level diagnosis of cervical pre-cancer at the point of care.
AbstractList	Goal: In this paper, we propose methods for (1) automatic feature extraction and classification for acetic acid and Lugol's iodine cervigrams and (2) methods for combining features/diagnosis of different contrasts in cervigrams for improved performance. Methods: We developed algorithms to pre-process pathology-labeled cervigrams and extract simple but powerful color and textural-based features. The features were used to train a support vector machine model to classify cervigrams based on corresponding pathology for visual inspection with acetic acid, visual inspection with Lugol's iodine, and a combination of the two contrasts. Results: The proposed framework achieved a sensitivity, specificity, and accuracy of 81.3%, 78.6%, and 80.0%, respectively, when used to distinguish cervical intraepithelial neoplasia (CIN+) relative to normal and benign tissues. This is superior to the average values achieved by three expert physicians on the same data set for discriminating normal/benign cases from CIN+ (77% sensitivity, 51% specificity, and 63% accuracy). Conclusion: The results suggest that utilizing simple color- and textural-based features from visual inspection with acetic acid and visual inspection with Lugol's iodine images may provide unbiased automation of cervigrams. Significance: This would enable automated, expert-level diagnosis of cervical pre-cancer at the point of care. In this paper, we propose methods for (1) automatic feature extraction and classification for acetic acid and Lugol's iodine cervigrams and (2) methods for combining features/diagnosis of different contrasts in cervigrams for improved performance.GOALIn this paper, we propose methods for (1) automatic feature extraction and classification for acetic acid and Lugol's iodine cervigrams and (2) methods for combining features/diagnosis of different contrasts in cervigrams for improved performance.We developed algorithms to pre-process pathology-labeled cervigrams and extract simple but powerful color and textural-based features. The features were used to train a support vector machine model to classify cervigrams based on corresponding pathology for visual inspection with acetic acid, visual inspection with Lugol's iodine, and a combination of the two contrasts.METHODSWe developed algorithms to pre-process pathology-labeled cervigrams and extract simple but powerful color and textural-based features. The features were used to train a support vector machine model to classify cervigrams based on corresponding pathology for visual inspection with acetic acid, visual inspection with Lugol's iodine, and a combination of the two contrasts.The proposed framework achieved a sensitivity, specificity, and accuracy of 81.3%, 78.6%, and 80.0%, respectively, when used to distinguish cervical intraepithelial neoplasia (CIN+) relative to normal and benign tissues. This is superior to the average values achieved by three expert physicians on the same data set for discriminating normal/benign cases from CIN+ (77% sensitivity, 51% specificity, and 63% accuracy).RESULTSThe proposed framework achieved a sensitivity, specificity, and accuracy of 81.3%, 78.6%, and 80.0%, respectively, when used to distinguish cervical intraepithelial neoplasia (CIN+) relative to normal and benign tissues. This is superior to the average values achieved by three expert physicians on the same data set for discriminating normal/benign cases from CIN+ (77% sensitivity, 51% specificity, and 63% accuracy).The results suggest that utilizing simple color- and textural-based features from visual inspection with acetic acid and visual inspection with Lugol's iodine images may provide unbiased automation of cervigrams.CONCLUSIONThe results suggest that utilizing simple color- and textural-based features from visual inspection with acetic acid and visual inspection with Lugol's iodine images may provide unbiased automation of cervigrams.This would enable automated, expert-level diagnosis of cervical pre-cancer at the point of care.SIGNIFICANCEThis would enable automated, expert-level diagnosis of cervical pre-cancer at the point of care. In this paper, we propose methods for (1) automatic feature extraction and classification for acetic acid and Lugol's iodine cervigrams and (2) methods for combining features/diagnosis of different contrasts in cervigrams for improved performance. We developed algorithms to pre-process pathology-labeled cervigrams and extract simple but powerful color and textural-based features. The features were used to train a support vector machine model to classify cervigrams based on corresponding pathology for visual inspection with acetic acid, visual inspection with Lugol's iodine, and a combination of the two contrasts. The proposed framework achieved a sensitivity, specificity, and accuracy of 81.3%, 78.6%, and 80.0%, respectively, when used to distinguish cervical intraepithelial neoplasia (CIN+) relative to normal and benign tissues. This is superior to the average values achieved by three expert physicians on the same data set for discriminating normal/benign cases from CIN+ (77% sensitivity, 51% specificity, and 63% accuracy). The results suggest that utilizing simple color- and textural-based features from visual inspection with acetic acid and visual inspection with Lugol's iodine images may provide unbiased automation of cervigrams. This would enable automated, expert-level diagnosis of cervical pre-cancer at the point of care.
Author	Venegas, Gino Asiedu, Mercy Nyamewaa Ramanujam, Nimmi Mueller, Jenna L. Sapiro, Guillermo Lam, Christopher T. Schmitt, John W. Simhal, Anish Chaudhary, Usamah
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Snippet	Goal: In this paper, we propose methods for (1) automatic feature extraction and classification for acetic acid and Lugol's iodine cervigrams and (2) methods... In this paper, we propose methods for (1) automatic feature extraction and classification for acetic acid and Lugol's iodine cervigrams and (2) methods for...
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SubjectTerms	Acetic acid Algorithms Automation Benign Cervix Cervix Uteri - diagnostic imaging Color Colposcopes colposcopy Computer-aided detection and diagnosis Diagnosis Early Detection of Cancer - instrumentation Early Detection of Cancer - methods Feature extraction Female global health Handheld computers Humans image acquisition Image Interpretation, Computer-Assisted - instrumentation Image Interpretation, Computer-Assisted - methods Inspection Iodine Lugol's iodine Machine Learning Pathology Phase shift keying Physicians Point-of-Care Systems Precancerous Conditions - diagnostic imaging predictive models segmentation Sensitivity Support vector machines Uterine Cervical Neoplasms - diagnostic imaging
Title	Development of Algorithms for Automated Detection of Cervical Pre-Cancers With a Low-Cost, Point-of-Care, Pocket Colposcope
URI	https://ieeexplore.ieee.org/document/8580569 https://www.ncbi.nlm.nih.gov/pubmed/30575526 https://www.proquest.com/docview/2261882287 https://www.proquest.com/docview/2159983472 https://pubmed.ncbi.nlm.nih.gov/PMC6581620
Volume	66
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