Segmentation of Overlapped Steatosis in Whole-Slide Liver Histopathology Microscopy Images

TL;DR

This paper introduces a high-resolution, automated method for segmenting overlapped steatosis regions in whole-slide liver histopathology images, improving accuracy and reproducibility over manual and existing automated techniques.

Contribution

The proposed approach combines tissue bounding box estimation, high-curvature point detection, and ellipse fitting to accurately segment overlapped steatosis in large microscopy images.

Findings

Effective segmentation of overlapped steatosis regions demonstrated on liver tissue images.

Method shows promise for improving steatosis quantification in clinical pathology.

Validated on samples from 11 patients with encouraging results.

Abstract

An accurate steatosis quantification with pathology tissue samples is of high clinical importance. However, such pathology measurement is manually made in most clinical practices, subject to severe reader variability due to large sampling bias and poor reproducibility. Although some computerized automated methods are developed to quantify the steatosis regions, they present limited analysis capacity for high resolution whole-slide microscopy images and accurate overlapped steatosis division. In this paper, we propose a method that extracts an individual whole tissue piece at high resolution with minimum background area by estimating tissue bounding box and rotation angle. This is followed by the segmentation and segregation of steatosis regions with high curvature point detection and an ellipse fitting quality assessment method. We validate our method with isolated and overlapped steatosis regions in liver tissue images of 11 patients. The experimental results suggest that our method is promising for enhanced support of steatosis quantization during the pathology review for liver disease treatment.