Dr. Nicholas Kurniawan Profile

Dr. Nicholas Kurniawan

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Proceedings Article | 2 April 2024 Poster + Paper

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

Xiaodan Xing, Chunling Tang, Yunzhe Guo, Nicholas Kurniawan, Guang Yang

Proceedings Volume 12930, 129301N (2024) https://doi.org/10.1117/12.3006022

KEYWORDS: Image segmentation, Microscopy, Analytical research, Biological research, Image processing, Image analysis, Stem cells

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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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