An Image Segmentation Method for Automatic Analysis of Skin Surface Structure in Atopic Dermatitis by the Impression Mold Technique

摘要

Allergic inflammation patients may develop or worsen due to basal sweating disturbance. Currently, the IMT (Impression Mold Technique), which is a quantitative measurement method that can detect the structure of the skin surface consisting of skin ridges and folds and sweating condition, is used to detect basal sweating. The detection of basal sweating using the IMT requires about 30 minutes because the specialist analyzes it visually using an optical microscope, and analysis errors may occur. Therefore, the purpose of this study is to develop a discriminator that automatically discriminates skin ridges and folds by performing image analysis using a discriminator that uses deep learning. We use U-Net as the discriminator and a data set including images of the thighs, forearm, chest, forehead, and other parts of healthy controls and atopic dermatitis. We evaluate the discriminator using the mDice co-efficient, which is the average of the Dice coefficient of the skin ridges and the Dice coefficient of the skin folds of each part. As a result, for 96 images of a test data, the average mDice coefficients for healthy controls and atopic dermatitis were 0.92 and 0.96, respectively.