DIC Image Segmentation of Dense Cell Populations by Combining Deep Learning and Watershed

Image segmentation of dense cell populations acquired using label-free optical microscopy techniques is a challenging problem. In this paper, we propose a novel approach based on a combination of deep learning and the watershed transform to segment differential interference contrast (DIC) images with high accuracy. The main idea of our approach is to train a convolutional neural network to detect both cellular markers and cellular areas and, based on these predictions, to split the individual cells using the watershed transform. The approach was developed based on the images of dense HeLa cell populations included in the Cell Tracking Challenge database. Our approach was ranked the best in terms of segmentation, detection, as well as overall performance as evaluated on the challenge datasets.