Development of a deep learning based image processing tool for enhanced organoid analysis
Abstract Contrary to 2D cells, 3D organoid structures are composed of diverse cell types and exhibit morphologies of various sizes. Although researchers frequently monitor morphological changes, analyzing every structure with the naked eye is difficult. Given that deep learning (DL) has been used fo...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2023-11-01
|
| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-023-46485-2 |
| _version_ | 1797559448017829888 |
|---|---|
| author | Taeyun Park Taeyul K. Kim Yoon Dae Han Kyung-A Kim Hwiyoung Kim Han Sang Kim |
| author_facet | Taeyun Park Taeyul K. Kim Yoon Dae Han Kyung-A Kim Hwiyoung Kim Han Sang Kim |
| author_sort | Taeyun Park |
| collection | DOAJ |
| description | Abstract Contrary to 2D cells, 3D organoid structures are composed of diverse cell types and exhibit morphologies of various sizes. Although researchers frequently monitor morphological changes, analyzing every structure with the naked eye is difficult. Given that deep learning (DL) has been used for 2D cell image segmentation, a trained DL model may assist researchers in organoid image recognition and analysis. In this study, we developed OrgaExtractor, an easy-to-use DL model based on multi-scale U-Net, to perform accurate segmentation of organoids of various sizes. OrgaExtractor achieved an average dice similarity coefficient of 0.853 from a post-processed output, which was finalized with noise removal. Correlation between CellTiter-Glo assay results and daily measured organoid images shows that OrgaExtractor can reflect the actual organoid culture conditions. The OrgaExtractor data can be used to determine the best time point for organoid subculture on the bench and to maintain organoids in the long term. |
| first_indexed | 2024-03-10T17:45:34Z |
| format | Article |
| id | doaj.art-0bf27a27fe07434686b2195c1942ddcc |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-03-10T17:45:34Z |
| publishDate | 2023-11-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-0bf27a27fe07434686b2195c1942ddcc2023-11-20T09:31:26ZengNature PortfolioScientific Reports2045-23222023-11-0113111110.1038/s41598-023-46485-2Development of a deep learning based image processing tool for enhanced organoid analysisTaeyun Park0Taeyul K. Kim1Yoon Dae Han2Kyung-A Kim3Hwiyoung Kim4Han Sang Kim5Department of Artificial Intelligence, Yonsei UniversityDepartment of Internal Medicine, Graduate School of Medical Science, Brain Korea 21 Project, Yonsei University College of MedicineDepartment of Surgery, Yonsei University College of MedicineDepartment of Internal Medicine, Graduate School of Medical Science, Brain Korea 21 Project, Yonsei University College of MedicineDepartment of Biomedical Systems Informatics, Yonsei University College of MedicineDepartment of Internal Medicine, Graduate School of Medical Science, Brain Korea 21 Project, Yonsei University College of MedicineAbstract Contrary to 2D cells, 3D organoid structures are composed of diverse cell types and exhibit morphologies of various sizes. Although researchers frequently monitor morphological changes, analyzing every structure with the naked eye is difficult. Given that deep learning (DL) has been used for 2D cell image segmentation, a trained DL model may assist researchers in organoid image recognition and analysis. In this study, we developed OrgaExtractor, an easy-to-use DL model based on multi-scale U-Net, to perform accurate segmentation of organoids of various sizes. OrgaExtractor achieved an average dice similarity coefficient of 0.853 from a post-processed output, which was finalized with noise removal. Correlation between CellTiter-Glo assay results and daily measured organoid images shows that OrgaExtractor can reflect the actual organoid culture conditions. The OrgaExtractor data can be used to determine the best time point for organoid subculture on the bench and to maintain organoids in the long term.https://doi.org/10.1038/s41598-023-46485-2 |
| spellingShingle | Taeyun Park Taeyul K. Kim Yoon Dae Han Kyung-A Kim Hwiyoung Kim Han Sang Kim Development of a deep learning based image processing tool for enhanced organoid analysis Scientific Reports |
| title | Development of a deep learning based image processing tool for enhanced organoid analysis |
| title_full | Development of a deep learning based image processing tool for enhanced organoid analysis |
| title_fullStr | Development of a deep learning based image processing tool for enhanced organoid analysis |
| title_full_unstemmed | Development of a deep learning based image processing tool for enhanced organoid analysis |
| title_short | Development of a deep learning based image processing tool for enhanced organoid analysis |
| title_sort | development of a deep learning based image processing tool for enhanced organoid analysis |
| url | https://doi.org/10.1038/s41598-023-46485-2 |
| work_keys_str_mv | AT taeyunpark developmentofadeeplearningbasedimageprocessingtoolforenhancedorganoidanalysis AT taeyulkkim developmentofadeeplearningbasedimageprocessingtoolforenhancedorganoidanalysis AT yoondaehan developmentofadeeplearningbasedimageprocessingtoolforenhancedorganoidanalysis AT kyungakim developmentofadeeplearningbasedimageprocessingtoolforenhancedorganoidanalysis AT hwiyoungkim developmentofadeeplearningbasedimageprocessingtoolforenhancedorganoidanalysis AT hansangkim developmentofadeeplearningbasedimageprocessingtoolforenhancedorganoidanalysis |