Ngo, Thi Kim Ngan and Yang, Sze Jue and Mao, Bin-Hsu and Nguyen, Thi Kim Mai and Ng, Qi Ding and Kuo, Yao-Lung and Tsai, Jui-Hung and Saw, Shier Nee and Tu, Ting-Yuan (2023) A deep learning-based pipeline for analyzing the influences of interfacial mechanochemical microenvironments on spheroid invasion using differential interference contrast microscopic images. Materials Today Bio, 23. ISSN 2590-0064, DOI sawsn@um.edu.my.
Full text not available from this repository.Abstract
Metastasis is the leading cause of cancer-related deaths. During this process, cancer cells are likely to navigate discrete tissue-tissue interfaces, enabling them to infiltrate and spread throughout the body. Three-dimensional (3D) spheroid modeling is receiving more attention due to its strengths in studying the invasive behavior of metastatic cancer cells. While microscopy is a conventional approach for investigating 3D invasion, post-invasion image analysis, which is a time-consuming process, remains a significant challenge for researchers. In this study, we presented an image processing pipeline that utilized a deep learning (DL) solution, with an encoder-decoder architecture, to assess and characterize the invasion dynamics of tumor spheroids. The developed models, equipped with feature extraction and measurement capabilities, could be successfully utilized for the automated segmentation of the invasive protrusions as well as the core region of spheroids situated within interfacial microenvironments with distinct mechanochemical factors. Our findings suggest that a combination of the spheroid culture and DL-based image analysis enable identification of time-lapse migratory patterns for tumor spheroids above matrix-substrate interfaces, thus paving the foundation for delineating the mechanism of local invasion during cancer metastasis.
| Item Type: | Article |
|---|---|
| Funders: | Southeast Asia and Taiwan Universities, University Advancement, College of Medicine, Catholic University of Korea, National Cheng Kung University, Core Research Laboratory, Ministry of Education, National Science and Technology Council [Grant no. MOST 111-2636-B-006-010, NSTC 112-2740-B-006-002 -, NSTC 112-2321-B-006-021, 111-2740-B-006-002, NSTC 112-2636-B-006-001 -] |
| Uncontrolled Keywords: | Deep learning; Spheroid; Interfacial invasion; DIC images; Image processing; Diepafitaxis |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science |
| Divisions: | Faculty of Computer Science & Information Technology > Department of Artificial Intelligence |
| Depositing User: | Ms. Juhaida Abd Rahim |
| Date Deposited: | 20 Oct 2025 07:25 |
| Last Modified: | 20 Oct 2025 07:25 |
| URI: | http://eprints.um.edu.my/id/eprint/48129 |
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