Lipid droplet detection in histopathological images using reinforcement learning

Abstract

The identification and analysis of lipid droplets in pathological images are pivotal owing to the variations in their size, shading and shape. The development of an artificial intelligence-based automatic detection method can facilitate the analysis of complex pathological images and provide essential support to pathologists. In this paper, we introduce a novel approach for the automated detection of lipid droplets, employing a limited set of images and a modest application of reinforcement learning (RL). Several filters tailored to lipid droplet size and contrast are used in combination. Through the combination of multiple filters using RL, potential lipid droplet regions are identified within pathological images. Subsequently, a random forest classifier is employed to distinguish between normal and lipid droplet images. Evaluation guided by the expertise of two pathologists with over 10 years of clinical experience indicated the hierarchical extraction of lipid droplets with consistent size and shading in pathological tissue images utilizing RL. The proposed method successfully detected lipid droplets in pathological images and facilitated the determination of both the quantity and distribution of lipid droplets within cells. The results highlight the efficacy of the approach in lipid droplet detection. This method is also useful for small to medium-sized fat droplets, which are relatively difficult for humans to detect from their morphology.

Keywords:

• Histopathological image
• lipid droplets
• Q-learning
• optimization
• random forest

Ethics statement

Experimental images were acquired while adhering to ethical research standards (Oita University Faculty of Medicine Ethics Committee approval no. 2568/June 15, 2023).

Author contributions

YH: Conceptualization, Data curation, Formal Analysis, Investigation, Methodology, Software, Validation, Visualization, Writing – original draft. HN: Conceptualization, Data curation, Formal Analysis, Investigation, Methodology, Project administration, Resources, Supervision, Validation, Writing – review & editing. KM: Conceptualization, Data curation, Formal Analysis, Investigation, Methodology, Project administration, Supervision, Validation, Writing – review & editing.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The data supporting the findings reported herein are available from the corresponding author, HN, upon reasonable request.

Additional information

Funding

The authors received no specific funding for this work.