A Fast and Accurate Algorithm for Nuclei Instance Segmentation in Microscopy Images

Abstract

Nuclei instance segmentation within microscopy images is a fundamental task in the pathology work-flow, based on that the meaningful nuclear features can be extracted and multiple biological related analysis can be performed. However, this task is still challenging because of the large variability among different types of nuclei. Although deep learning(DL) based methods have achieved state-of-the-art results in nuclei instance segmentation tasks, these methods are usually focus on improving the accuracy and require support of powerful computing resources. In this paper, we joint the detection and segmentation simultaneously, and propose a fast and accurate box-based nuclei instance segmentation method. Mainly, we employ a fusion module based on the feature pyramid network(FPN) to combine the complementary information of the shallow layers with deep layers for detection the nuclear location by bounding boxes. Subsequently, we crop the feature maps according to the bounding boxes and feed the cropped patches into an U-net architecture as a guide to separate clustered nuclei. The experiments show that the proposed approach outperforms prior state-of-the-art methods, not only on accuracy but also on speed. The source code will be released at: https://github.com/QUAPNH/Nucleiseg.