SegmentAnything helps microscopy images based automatic and quantitative organoid detection and analysis
arxiv(2023)
摘要
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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