Convolutional Neural Network for Segmentation of Single Cell Gel Electrophoresis Assay

The single cell gel electrophoresis assay, which is also referred to as the comet assay, is a quantitative method by which visual evidence of DNA damage in individual cells may be measured. Since this assay is sensitive and simple to perform, it is widely used in several areas including human biomonitoring, genotoxicology, and ecological monitoring. In the last decades, various computer systems have implemented segmentation algorithms based on traditional threshold techniques rather than efficient deep learning methods to automatically identify cells in comet assay output images. This paper presents a fully convolutional neural network based system, named U-NetComet, to automate comets segmentation, minimizing user interaction and providing reproducible measurements. A comparison of our method with a commercial system has been performed, and results showed that our system is more efficient and reliable.