Detail preserving conditional random field as 2-D RNN for gland segmentation in histology images

Abstract

Grading of cancer offers crucial insights for treatment planning. Morphology of glands in histology images is of prime importance for grading several types of cancers. Therefore, accurate segmentation of glands plays a pivotal role in planning the treatment in case of such cancers. We introduce a first-of-its-kind detail preserving conditional random field for gland segmentation from histology images. Our design involves a novel formulation of Gibbs energy that captures the spatial interaction between neighboring pixels through the hidden state of a 2-D recurrent neural network (2-D RNN). We show that the iterative training of the 2-D RNN results in the minimization of the Gibbs energy leading to accurate gland segmentation. Experiments on publicly available histology image datasets show the efficacy of the proposed method in accurate gland segmentation. Our model achieves at least 7% improvement in terms of Hausdorff distance for gland segmentation compared to a number of state-of-the-art techniques.