Automated Lesion Segmentation and Quantitative Analysis of Nevus in Whole-Face Images
| Autor: | Mingang Chen, Wei Chen, Chai Yuanhao, Yong Hu, Yan Zhang, Haisong Xu, Gang Chai |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Adult
medicine.medical_specialty Skin Neoplasms Adolescent Dermoscopy Logistic regression Diagnosis Differential Young Adult 03 medical and health sciences 0302 clinical medicine Humans Medicine Nevus Segmentation Diagnosis Computer-Assisted skin and connective tissue diseases 030223 otorhinolaryngology Survival rate business.industry Melanoma 030206 dentistry General Medicine Middle Aged medicine.disease Otorhinolaryngology Sample size determination Face (geometry) Surgery Radiology Differential diagnosis business Algorithms |
| Zdroj: | Journal of Craniofacial Surgery. 31:360-363 |
| ISSN: | 1049-2275 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|