BSMatch: Boundary Segmentation and Matching for Lipid Droplet Quantification in Diagnosis of Non-Alcoholic Fatty Liver Disease.

Abstract

Hepatic steatosis is one of the most obvious indicators of nonalcoholic fatty liver disease. However, the presence of many regions with a similar color and shape as lipid droplets in the histopathological image complicates the task of detecting genuine lipid droplets using automated methods. Accordingly, the present study proposes a boundary segmentation and matching (BSMatch) algorithm for the segmentation of lipid droplets based on their unique boundary characteristics. A two-branch RnB-Unet model is trained to segment the regions and boundaries of the droplets, respectively, in accordance with a boundary matching (BM) loss which enforces the consistency between them. A boundary matching score (BMS) measure is then used to improve the precision of the instance segmentation evaluation process by discarding segmented regions which are not well-matched with their predicted boundaries. The experimental results obtained using a H&E-stained liver slide dataset show that BSMatch outperforms existing methods in terms of both the IoU and the F1-score. The BSMatch results are used to predict the fat percentage in hepatocytes (FPH) in liver whole slide images. The predicted FPH values are well correlated with the steatosis grades assigned by experienced pathologists. Thus, BSMatch appears to have significant promise for NAFLD diagnosis in clinical contexts.