Automated Lesion Segmentation and Quantitative Analysis of Nevus in Whole-Face Images. Issue 2 (March 2020)

• Record Type: Journal Article
• Authors: Chen, Wei; Chai, Yuanhao; Chai, Gang; Hu, Yong; Chen, Mingang; Xu, Haisong; Zhang, Yan
• Is Part Of: Journal of craniofacial surgery. Volume 31:Issue 2(2020)
• Publication Date: 2020-03
• Subjects: Computer-assisted diagnosis -- controlled clinical trial -- image processing -- mass screening -- nevus; Facial bones -- Surgery -- Periodicals; Skull -- Surgery -- Periodicals; Face -- Surgery -- Periodicals; Surgery, Plastic -- Periodicals; 617.52
• DOI: 10.1097/SCS.0000000000006017
• Languages: English
• ISSNs: 1049-2275

Abstract

Abstract : Background: Nevus is very common; however, melanoma is slightly related to the deterioration of nevus because of its vulnerability to solarization, friction, aging, heredity, and other factors. Early diagnosis is essential for melanoma treatment, since patients have a high survival rate with early detection and treatment. Computer-aided diagnosis has been applied in the differential diagnosis of melanoma and benign nevi and achieved high accuracy, but it does not suit the screening of nevi because most studies are based on dermoscopy with a narrow field of vision and performed by professional doctors. Therefore, this study aimed to present the accuracy and effectiveness of our algorithm. Methods: Based on whole-face images of patients, the authors used logistic regression and the Newton method to detect the nevus region. Then, Python and OpenCV were employed to detect the lesion edge and compute the area of the regions. A multicenter clinical trial with a sample size of 600 was then conducted to evaluate the effectiveness of the algorithm. Results: The algorithm detected 2672 nevi from 600 patients, in which there were 195 patients of missed diagnosis and 310 patients of misdiagnosis. The Kappa value between 2 groups was 0.860 (>0.8). Paired t -test showed no significant difference between 2 groups' area results ( P = 0.265, P > 0.05). Conclusion: Within the limitations of this study, the authors demonstrated a high agreement between algorithm's detection and doctor's diagnosis. Our new algorithm has great effectiveness in nevus detection, edge segmentation, and area measurement. Abstract : Supplemental Digital Content is available in the text