  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.
