A unified two-parallel-branch deep neural network for joint gland contour and segmentation learning

Abstract Existing state-of-the-art gland segmentation methods usually extract different high-level features from shared low-level layers in a deep framework for separately learning gland segmentation and contour prediction and fusing the results. Such an architecture does not fully respect the complementary relationship between the two tasks, and the independency between the two kinds of task-specific features, which are meant to depict different parts of gland objects. To address the issues, we propose here a new unified end-to-end trainable deep neural network. It consists of two parallel branches, each extracts high-level features from separate low-level feature maps for a specific task under deep supervision. The gland segmentation and contour learning are jointly performed based on combined features of the two branches, while their correlations are explored through feature propagation. Besides, the proposed architecture better facilitates leveraging the power of transfer learning, which alleviates the quandary of insufficient training data and eases the learning process by weight migration from multiple task specific pre-trained models. Experiments on the benchmark dataset of 2015 MICCAI Gland Segmentation Challenge show that the proposed method delivers superior performance over the state-of-the-art approaches.