A Survey of Deep Learning in Histopathological Nuclear Segmentation

Histopathological images contain rich information that can be used to diagnose and monitor disease progression and to predict patient survival. Accurate morphological segmentation, in particular nuclear segmentation, of these images is a critical task, where many deep learning (DL) based methods have been widely used. This article provides a thorough review of these methods from three different aspects: nuclear semantic segmentation, nuclear instance segmentation, and joint nuclear segmentation and classification. It also summarizes the process of whole-slide image nuclear segmentation, followed by an extensive compilation of publicly available datasets and evaluation metrics that have been widely used to evaluate the DL based methods. Finally, the challenges and future directions in this field are discussed. This survey is expected to serve as a practical guide for researchers interested in using and improving DL-based models for histopathological nuclear segmentation.