Liver Steatosis Segmentation with Deep Learning Methods

TL;DR

This paper presents a deep learning approach using Mask-RCNN to accurately segment overlapped liver steatosis droplets in histopathological images, aiding in liver disease diagnosis.

Contribution

The study applies Mask-RCNN with transfer learning for precise segmentation of overlapped steatosis regions, improving upon previous methods.

Findings

Achieved 75.87% Average Precision in segmentation

Attained 60.66% Recall for overlapped droplets

Secured 65.88% F1-score indicating balanced performance

Abstract

Liver steatosis is known as the abnormal accumulation of lipids within cells. An accurate quantification of steatosis area within the liver histopathological microscopy images plays an important role in liver disease diagnosis and trans-plantation assessment. Such a quantification analysis often requires a precise steatosis segmentation that is challenging due to abundant presence of highly overlapped steatosis droplets. In this paper, a deep learning model Mask-RCNN is used to segment the steatosis droplets in clumps. Extended from Faster R-CNN, Mask-RCNN can predict object masks in addition to bounding box detection. With transfer learning, the resulting model is able to segment overlapped steatosis regions at 75.87% by Average Precision, 60.66% by Recall,65.88% by F1-score, and 76.97% by Jaccard index, promising to support liver disease diagnosis and allograft rejection prediction in future clinical practice.