Gland Segmentation in Histopathology Images Using Random Forest Guided Boundary Construction

TL;DR

This paper presents an automated gland segmentation method in histopathology images using a random forest-guided boundary construction approach, addressing challenges posed by gland variability in shape, size, and texture.

Contribution

The paper introduces a novel boundary construction technique guided by random forest classification for accurate gland segmentation in complex histopathology images.

Findings

Effective segmentation of glands with diverse shapes and textures

Improved boundary detection accuracy over traditional methods

Automated process reduces observer bias and time

Abstract

Grading of cancer is important to know the extent of its spread. Prior to grading, segmentation of glandular structures is important. Manual segmentation is a time consuming process and is subject to observer bias. Hence, an automated process is required to segment the gland structures. These glands show a large variation in shape size and texture. This makes the task challenging as the glands cannot be segmented using mere morphological operations and conventional segmentation mechanisms. In this project we propose a method which detects the boundary epithelial cells of glands and then a novel approach is used to construct the complete gland boundary. The region enclosed within the boundary can then be obtained to get the segmented gland regions.