AN INNOVATIVE APPLICATION OF COMPUTERIZED CELL IMAGE ANALYSIS IN THE THERAPEUTIC AND CLINICAL FOLLOW UP — QUANTITATIVE EVALUATION OF THE CLINICOPATHOLOGICAL IMPROVEMENT IN A CASE OF CUTANEOUS MASTOCYTOSIS RECEIVING A SERIES OF UVA1 IRRADIATION

Abstract

The severity and outcome of mastocytosis are known to be related to the number and size of mast cells. The standard treatment of this rare dermatological disorder is phototherapy with UVA1 irradiation. Here, the treatment effects were evaluated during the course of phototherapy on a patient with mastocytosis (urticaria pigmentosa). Photomicrographs of CD117 antibody-stained histological sections of the dermis were analyzed using image software programmed with artificial neural networks. The artificial neural networks analyzed the color feature of epidermis, dermis and mast cells. First, mast cells were isolated from epidermis and dermis, and RGB pixels were converted to HSV color space for artificial neural networks training. Secondly, morphology and connective component labeling were used in epidermal pixels classification to determine the scope of dermal areas. Thirdly, regional separation isolated the dermis for the mast cell analysis. Specifically, the number and size of mast cells were quantified. The method has good reproducibility compared with other methods which had larger variance and lower accuracy. We found also that the UVA1 phototherapy was effective as indicated by a post-treatment change in the ratio of mast cell surface to cell number. In addition, we compared similar results based on subjective visual inspection (from 10 human subjects). Both methods of analysis showed a reduction in the number and size of mast cells after phototherapy. But compared with human judgment, the computerized method was superior in accuracy, sensitivity, specificity and reproducibility of the findings.