Nuclei & Glands Instance Segmentation in Histology Images: A Narrative Review

TL;DR

This paper reviews recent AI-based methods for nuclei and glands instance segmentation in histology images, highlighting advancements, limitations, datasets, challenges, and future research directions to facilitate clinical applications in cancer diagnosis and treatment.

Contribution

It provides the first comprehensive review of instance segmentation methods in histology images, analyzing 126 papers, datasets, challenges, and proposing future research pathways.

Findings

Analysis of 126 recent papers on segmentation methods

Summary of publicly available datasets and challenges

Identification of limitations and future research directions

Abstract

Instance segmentation of nuclei and glands in the histology images is an important step in computational pathology workflow for cancer diagnosis, treatment planning and survival analysis. With the advent of modern hardware, the recent availability of large-scale quality public datasets and the community organized grand challenges have seen a surge in automated methods focusing on domain specific challenges, which is pivotal for technology advancements and clinical translation. In this survey, 126 papers illustrating the AI based methods for nuclei and glands instance segmentation published in the last five years (2017-2022) are deeply analyzed, the limitations of current approaches and the open challenges are discussed. Moreover, the potential future research direction is presented and the contribution of state-of-the-art methods is summarized. Further, a generalized summary of publicly available datasets and a detailed insights on the grand challenges illustrating the top performing methods specific to each challenge is also provided. Besides, we intended to give the reader current state of existing research and pointers to the future directions in developing methods that can be used in clinical practice enabling improved diagnosis, grading, prognosis, and treatment planning of cancer. To the best of our knowledge, no previous work has reviewed the instance segmentation in histology images focusing towards this direction.