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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Vydáno v:IEEE transactions on biomedical engineering Ročník 66; číslo 8; s. 2306 - 2318
Hlavní autoři: 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:angličtina
Vydáno: United States IEEE 01.08.2019
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
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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Shrnutí: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.
Bibliografie:ObjectType-Article-1
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ISSN:0018-9294
1558-2531
1558-2531
DOI:10.1109/TBME.2018.2887208