Contour-Aware Residual W-Net for Nuclei Segmentation.

Nuclei segmentation is an important pre-processing step for any vision based cytopathological diagnostic system which extracts information from nuclei to perform tasks such as cancer detection. A cell nuclei segmentation pipeline should be robust, accurate and fast. We propose a deep learning based model, Contour-Aware Residual W-Net (WRC-Net), which consists of double U-Net, [5] or W-Net. The first U-Net learns to predict nuclei boundaries and the second generates the segmentation map. Our model can accurately segment a 128x128 dimensional image in less than 0.05s. Our model can learn from a very limited training data with as low as a single training image. We tested our model on real HE (Hematoxylin and Eosin) stained cell images and it showed better overall performance against previous state-of-the-art nuclei segmentation methods. (C) 2019 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review under responsibility of KES International.