Improving Nuclei/Gland Instance Segmentation in Histopathology Images by Full Resolution Neural Network and Spatial Constrained Loss
Image segmentation plays an important role in pathology image analysis as the accurate separation of nuclei or glands is crucial for cancer diagnosis and other clinical analyses. The networks and cross entropy loss in current deep learning based segmentation methods originate from image classification tasks and have drawbacks for segmentation. In this paper, we propose a full resolution convolutional neural network (FullNet) that maintains full resolution feature maps to improve the localization accuracy. We also propose a variance constrained cross entropy (varCE) loss that encourages the network to learn the spatial relationship between pixels in the same instance. Experiments on a nuclei segmentation dataset and the 2015 MICCAI Gland Segmentation Challenge dataset show that the proposed FullNet with the varCE loss achieves state of the art performance. The code is publicly available (https:/github.com/huiqu18/FullNet varCE)
MICCAI