SegmentAnything helps microscopy images based automatic and quantitative organoid detection and analysis
This work addresses the need for automated and quantitative organoid analysis in fields like drug discovery and organ development, though it is incremental as it applies an existing segmentation model to a new domain.
The paper tackled the labor-intensive manual analysis of organoid morphology in microscopy images by proposing a pipeline using SegmentAnything for precise organoid demarcation and introducing quantitative morphological properties, resulting in automatic detection and measurement that closely aligns with manual methods.
Organoids are self-organized 3D cell clusters that closely mimic the architecture and function of in vivo tissues and organs. Quantification of organoid morphology helps in studying organ development, drug discovery, and toxicity assessment. Recent microscopy techniques provide a potent tool to acquire organoid morphology features, but manual image analysis remains a labor and time-intensive process. Thus, this paper proposes a comprehensive pipeline for microscopy analysis that leverages the SegmentAnything to precisely demarcate individual organoids. Additionally, we introduce a set of morphological properties, including perimeter, area, radius, non-smoothness, and non-circularity, allowing researchers to analyze the organoid structures quantitatively and automatically. To validate the effectiveness of our approach, we conducted tests on bright-field images of human induced pluripotent stem cells (iPSCs) derived neural-epithelial (NE) organoids. The results obtained from our automatic pipeline closely align with manual organoid detection and measurement, showcasing the capability of our proposed method in accelerating organoids morphology analysis.