A Deep Model with Shape-Preserving Loss for Gland Instance Segmentation

Abstract

Segmenting gland instance in histology images requires not only separating glands from a complex background but also identifying each gland individually via accurate boundary detection. This is a very challenging task due to lots of noises from the background, tiny gaps between adjacent glands, and the “coalescence” problem arising from adhesive gland instances. State-of-the-art methods adopted multi-channel/multi-task deep models to separately accomplish pixel-wise gland segmentation and boundary detection, yielding a high model complexity and difficulties in training. In this paper, we present a unified deep model with a new shape-preserving loss which facilities the training for both pixel-wise gland segmentation and boundary detection simultaneously. The proposed shape-preserving loss helps significantly reduce the model complexity and make the training process more controllable. Compared with the current state-of-the-art methods, the proposed deep model with the shape-preserving loss achieves the best overall performance on the 2015 MICCAI Gland Challenge dataset. In addition, the flexibility of integrating the proposed shape-preserving loss into any learning based medical image segmentation networks offers great potential for further performance improvement of other applications.