An Automatic Nuclei Image Segmentation Based on Multi-Scale Split-Attention U-Net

Qing Xu, Wenting Duan

Abstract:

Nuclei segmentation is an important step in the task of medical image analysis. Nowadays,
deep learning techniques based on Convolutional Neural Networks (CNNs) have become
prevalent methods in nuclei segmentation. In this paper, we propose a network called
Multi-scale Split-Attention U-Net (MSAU-Net) for further improving the performance of
cell segmentation. MSAU-Net is based on U-Net architecture and the original blocks used to
down-sampling and up-sampling paths are replaced with Multi-scale Split-Attention blocks
for capturing independent semantic information of nuclei images. A public microscopy image
dataset from 2018 Data Science Bowl grand challenge is selected to train and evaluate
MSAU-Net. By running trained models on the test set, our model reaches average Intersection
over Union (IoU) of 0.851, which is better than other prominent models, especially
4.8 percent higher than the original U-Net. For other evaluation metrics including accuracy,
precision, recall and F1-score, MSAU-Net shows better performance in the most of
indicators. The outstanding result reveals that our proposed model presents a promising
nuclei segmentation method for the microscopy image analysis.