Deep Segmentation-Emendation Model for Gland Instance Segmentation

Accurate and automated gland instance segmentation on histology microscopy images can assist pathologists to diagnose the malignancy degree of colorectal adenocarcinoma. To address this problem, many deep convolutional neural network (DCNN) based methods have been proposed, most of which aim to generate better segmentation by improving the model structure and loss function. Few of them, however, focus on further emendating the inferred predictions, thus missing a chance to refine the obtained segmentation results. In this paper, we propose the deep segmentation-emendation (DSE) model for gland instance segmentation. This model consists of a segmentation network (Seg-Net) and an emendation network (Eme-Net). The Seg-Net is dedicated to generating segmentation results, and the Eme-Net learns to predict the inconsistency between the ground truth and the segmentation results generated by Seg-Net. The predictions made by Eme-Net can in turn be used to refine the segmentation result. We evaluated our DSE model against five recent deep learning models on the 2015 MIC-CAI Gland Segmentation challenge (GlaS) dataset and against two deep learning models on the colorectal adenocarcinoma (CRAG) dataset. Our results indicate that using Eme-Net results in significant improvement in segmentation accuracy, and the proposed DSE model is able to substantially outperform all the rest models in gland instance segmentation on both datasets.