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

• URL: http://arxiv.org/abs/2309.04190v4
• Date: Mon, 8 Apr 2024 10:57:42 GMT
• Title: SegmentAnything helps microscopy images based automatic and quantitative organoid detection and analysis
• Abstract summary: Organoids are self-organized 3D cell clusters that closely mimic the architecture and function of in vivo tissues and organs. Recent microscopy techniques provide a potent tool to acquire organoid morphology features, but manual image analysis remains a labor and time-intensive process. This paper proposes a comprehensive pipeline for microscopy analysis that leverages the SegmentAnything to precisely demarcate individual organoids.
• Score: 2.9074136720945356
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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