Nuclei probability and centroid map network for nuclei instance segmentation in histology images

Nuclei instance segmentation is an integral step in digital pathology workflow as it is a prerequisite for most downstream tasks such as patient survival analysis, precision medicine, and cancer prognosis. There exist many challenges such as quality of labeled data, staining variation among tissue slides, high variation among multi-organ & multi-center digital slides and overlapping nuclei that are difficult to separate. Therefore, it is important to have an automatic and robust nuclei instance segmentation model that saves the time of pathologists by delineating accurate nuclei instances. To this end, we develop a nuclei instance segmentation pipeline that estimates distance transform and nuclear masks using an encoder-decoder-based CNN model. These estimated distance transform and nuclear masks are then utilized to delineate accurate nuclei boundaries from overlapping nuclei. We demonstrate that our proposed NC-Net model is lightweight and produces state-of-the-art results on the three recently published largest nuclei instance segmentation datasets to date. Additionally, our proposed NC-Net model is faster and utilizes a fewer number of parameters for learning as compared to other top-performing nuclei instance segmentation models. The purpose of developing a lightweight and state-of-the-art model is to provide capacity building to digital pathology workflows by reducing inference times and delineating accurate nuclear instances. The implementation details and the trained models are made available at this https://github.com/nauyan/NC-Net.

Automated summary

Nuclei instance segmentation is crucial for various downstream tasks in digital pathology. However, there are challenges such as staining variation, overlapping nuclei, and limited labeled data. In this paper, a lightweight and state-of-the-art NC-Net model is proposed to address these challenges, achieving accurate and fast nuclei instance segmentation.