Lipid droplet distribution quantification method based on lipid droplet detection by constrained reinforcement learning - Yoshitomi Harada; Haruto Nishida; Keiko Matsuura (PLoS ONE, 2025)
We previously proposed the lipid droplet detection by reinforcement learning (LiDRL) method using a limited dataset of pathological images. The method automatically detects lipid droplets using reinforcement learning to optimize filter combinations based on their size and grayscale contrast. In this study, we aimed to detect lipid droplets reliably and analyze their distribution patterns across pathological tissue images. For this purpose, we improved the environmental and agent-side functions in LiDRL to obtain a revised method. These improvements increased the stability and robustness of the system, enabling consistent extraction of lipid droplets of similar sizes across all rank levels in the pathological tissue images. We quantified the lipid droplet distribution using average probability density and entropy and visualized it as a heat map. This analysis facilitates the extraction of lipid droplet characteristics that could serve as indicators of liver disease.