U-shaped Feature Extractor Used on Mask R-CNN for Cell Nuclei Image Segmentation

Abstract

The cell nuclei segmentation of is a challenging task in microscopy image analysis. The problems of noise, small cell nuclei, and few training data samples in the data set will all affect the effectiveness of the model to varying degrees. This paper presents a new approach to nuclear image segmentation based on convolutional neural networks. Our approach is based on Mask R-CNN with some modification, which combines low-level semantic features for model training. In order to make the model specific to each low-level feature map, the attention mechanism was used to assign weights to each low-level feature map, making the model learning more purposeful. Our method achieves an average precision value of 62.8%, which is 2.7% higher than that of the Mask R-CNN (ResNet50) basic model and 6.3% of the Mask R-CNN (ResNet101) basic model.