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

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

doi:10.1117/12.3006022

2 April 2024 SegmentAnything helps microscopy images based on automatic and quantitative organoid detection and analysis

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

Author Affiliations +

Proceedings Volume 12930, Medical Imaging 2024: Clinical and Biomedical Imaging; 129301N (2024) https://doi.org/10.1117/12.3006022
Event: SPIE Medical Imaging, 2024, San Diego, California, United States

Conference Poster

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.

Citation Download Citation

Xiaodan Xing, Chunling Tang, Yunzhe Guo, Nicholas Kurniawan, and Guang Yang "SegmentAnything helps microscopy images based on automatic and quantitative organoid detection and analysis", Proc. SPIE 12930, Medical Imaging 2024: Clinical and Biomedical Imaging, 129301N (2 April 2024); https://doi.org/10.1117/12.3006022

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available