Delineation of Skin Strata in Reflectance Confocal Microscopy Images with Recurrent Convolutional Networks

Abstract

Reflectance confocal microscopy (RCM) is an effective, non-invasive pre-screening tool for cancer diagnosis. However, acquiring and reading RCM images requires extensive training and experience, and novice clinicians exhibit high variance in diagnostic accuracy. Consequently, there is a compelling need for quantitative tools to standardize image acquisition and analysis. In this study, we use deep recurrent convolutional neural networks to delineate skin strata in stacks of RCM images collected at consecutive depths. To perform diagnostic analysis, clinicians collect RCM images at 4-5 specific layers in the tissue. Our model automates this process by discriminating between RCM images of different layers. Testing our model on an expert labeled dataset of 504 RCM stacks, we achieve 87.97% classification accuracy, and a 9-fold reduction in the number of anatomically impossible errors compared to the previous state-of-the-art.