SegmentAnything helps microscopy images based automatic and quantitative organoid detection and analysis

TL;DR

This paper introduces an automated microscopy analysis pipeline using SegmentAnything for precise organoid detection and quantification of morphological features, significantly reducing manual effort in organoid research.

Contribution

The study presents a novel automated pipeline leveraging SegmentAnything for accurate organoid segmentation and introduces new morphological metrics for quantitative analysis.

Findings

Automatic detection closely matches manual measurements

Pipeline accelerates organoid morphology analysis

Effective on bright-field images of iPSC-derived organoids

Abstract

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.