Segmentation, tracking, and sub-cellular feature extraction in 3D time-lapse images
Abstract This paper presents a method for time-lapse 3D cell analysis. Specifically, we consider the problem of accurately localizing and quantitatively analyzing sub-cellular features, and for tracking individual cells from time-lapse 3D confocal cell image stacks. The heterogeneity of cells and th...
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2023-03-01
|
| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-023-29149-z |
| _version_ | 1797865047674847232 |
|---|---|
| author | Jiaxiang Jiang Amil Khan S. Shailja Samuel A. Belteton Michael Goebel Daniel B. Szymanski B. S. Manjunath |
| author_facet | Jiaxiang Jiang Amil Khan S. Shailja Samuel A. Belteton Michael Goebel Daniel B. Szymanski B. S. Manjunath |
| author_sort | Jiaxiang Jiang |
| collection | DOAJ |
| description | Abstract This paper presents a method for time-lapse 3D cell analysis. Specifically, we consider the problem of accurately localizing and quantitatively analyzing sub-cellular features, and for tracking individual cells from time-lapse 3D confocal cell image stacks. The heterogeneity of cells and the volume of multi-dimensional images presents a major challenge for fully automated analysis of morphogenesis and development of cells. This paper is motivated by the pavement cell growth process, and building a quantitative morphogenesis model. We propose a deep feature based segmentation method to accurately detect and label each cell region. An adjacency graph based method is used to extract sub-cellular features of the segmented cells. Finally, the robust graph based tracking algorithm using multiple cell features is proposed for associating cells at different time instances. We also demonstrate the generality of our tracking method on C. elegans fluorescent nuclei imagery. Extensive experiment results are provided and demonstrate the robustness of the proposed method. The code is available on GitHub and the method is available as a service through the BisQue portal. |
| first_indexed | 2024-04-09T23:01:43Z |
| format | Article |
| id | doaj.art-649a2299fdfc402e94b9440c8db276e7 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-04-09T23:01:43Z |
| publishDate | 2023-03-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-649a2299fdfc402e94b9440c8db276e72023-03-22T10:55:16ZengNature PortfolioScientific Reports2045-23222023-03-0113111410.1038/s41598-023-29149-zSegmentation, tracking, and sub-cellular feature extraction in 3D time-lapse imagesJiaxiang Jiang0Amil Khan1S. Shailja2Samuel A. Belteton3Michael Goebel4Daniel B. Szymanski5B. S. Manjunath6Department of Electrical and Computer Engineering, University of CaliforniaDepartment of Electrical and Computer Engineering, University of CaliforniaDepartment of Electrical and Computer Engineering, University of CaliforniaDepartment of Botany and Plant Pathology, Purdue UniversityDepartment of Electrical and Computer Engineering, University of CaliforniaDepartment of Botany and Plant Pathology, Purdue UniversityDepartment of Electrical and Computer Engineering, University of CaliforniaAbstract This paper presents a method for time-lapse 3D cell analysis. Specifically, we consider the problem of accurately localizing and quantitatively analyzing sub-cellular features, and for tracking individual cells from time-lapse 3D confocal cell image stacks. The heterogeneity of cells and the volume of multi-dimensional images presents a major challenge for fully automated analysis of morphogenesis and development of cells. This paper is motivated by the pavement cell growth process, and building a quantitative morphogenesis model. We propose a deep feature based segmentation method to accurately detect and label each cell region. An adjacency graph based method is used to extract sub-cellular features of the segmented cells. Finally, the robust graph based tracking algorithm using multiple cell features is proposed for associating cells at different time instances. We also demonstrate the generality of our tracking method on C. elegans fluorescent nuclei imagery. Extensive experiment results are provided and demonstrate the robustness of the proposed method. The code is available on GitHub and the method is available as a service through the BisQue portal.https://doi.org/10.1038/s41598-023-29149-z |
| spellingShingle | Jiaxiang Jiang Amil Khan S. Shailja Samuel A. Belteton Michael Goebel Daniel B. Szymanski B. S. Manjunath Segmentation, tracking, and sub-cellular feature extraction in 3D time-lapse images Scientific Reports |
| title | Segmentation, tracking, and sub-cellular feature extraction in 3D time-lapse images |
| title_full | Segmentation, tracking, and sub-cellular feature extraction in 3D time-lapse images |
| title_fullStr | Segmentation, tracking, and sub-cellular feature extraction in 3D time-lapse images |
| title_full_unstemmed | Segmentation, tracking, and sub-cellular feature extraction in 3D time-lapse images |
| title_short | Segmentation, tracking, and sub-cellular feature extraction in 3D time-lapse images |
| title_sort | segmentation tracking and sub cellular feature extraction in 3d time lapse images |
| url | https://doi.org/10.1038/s41598-023-29149-z |
| work_keys_str_mv | AT jiaxiangjiang segmentationtrackingandsubcellularfeatureextractionin3dtimelapseimages AT amilkhan segmentationtrackingandsubcellularfeatureextractionin3dtimelapseimages AT sshailja segmentationtrackingandsubcellularfeatureextractionin3dtimelapseimages AT samuelabelteton segmentationtrackingandsubcellularfeatureextractionin3dtimelapseimages AT michaelgoebel segmentationtrackingandsubcellularfeatureextractionin3dtimelapseimages AT danielbszymanski segmentationtrackingandsubcellularfeatureextractionin3dtimelapseimages AT bsmanjunath segmentationtrackingandsubcellularfeatureextractionin3dtimelapseimages |