Segmentation of Overlapped Steatosis in Whole-Slide Liver Histopathology Microscopy Images
This addresses the need for automated, accurate steatosis quantification in liver disease treatment, though it appears incremental as it builds on existing computerized methods.
The paper tackled the problem of quantifying steatosis in whole-slide liver histopathology images, which is manually done and prone to variability, by proposing a method that segments and segregates overlapped steatosis regions, validated on images from 11 patients.
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.