Automated Lesion Segmentation and Quantitative Analysis of Nevus in Whole-Face Images.

Automated Lesion Segmentation and Quantitative Analysis of Nevus in Whole-Face Images.

Publication date: Nov 12, 2019

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.

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.

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).

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.

Chen, W., Chai, Y., Chai, G., Hu, Y., Chen, M., Xu, H., and Zhang, Y. Automated Lesion Segmentation and Quantitative Analysis of Nevus in Whole-Face Images. 24768. 2019 J Craniofac Surg.

Concepts Keywords
Aging Diagnosis algorithm
Algorithm Effectiveness algorithm
Benign Present effectiveness algorithm
Clinical Trial Melanoma treatment
Dermoscopy Agreement algorithm
Differential Diagnosis Melanoma
Friction Great effectiveness nevus
Heredity Cutaneous conditions
Lesion Organ systems
Logistic Regression Melanoma
Melanoma Dermatology
Nevi Nevus
Nevus Dermatoscopy
Newton Halo nevus
OpenCV Dysplastic nevus syndrome
Python Effectiveness algorithm
Solarization Diagnosis algorithm
Present effectiveness algorithm
High agreement algorithm

Semantics

Type Source Name
disease MESH Nevus
disease DOID Face
gene UNIPROT ELOVL6
gene UNIPROT FANCE
disease MESH melanoma
disease DOID melanoma
pathway BSID Melanoma
disease MESH aging
pathway BSID Aging
disease MESH diagnosis
disease MESH misdiagnosis
gene UNIPROT RXFP2

Original Article

Leave a Comment

Your email address will not be published. Required fields are marked *