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McAfee L, Heath Z, Anderson W, Hozi M, Orr JW, Kang YA. The development of an automated microscope image tracking and analysis system. Biotechnol Prog 2024:e3490. [PMID: 38888043 DOI: 10.1002/btpr.3490] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2024] [Revised: 05/29/2024] [Accepted: 06/09/2024] [Indexed: 06/20/2024]
Abstract
Microscopy image analysis plays a crucial role in understanding cellular behavior and uncovering important insights in various biological and medical research domains. Tracking cells within the time-lapse microscopy images is a fundamental technique that enables the study of cell dynamics, interactions, and migration. While manual cell tracking is possible, it is time-consuming and prone to subjective biases that impact results. In order to solve this issue, we sought to create an automated software solution, named cell analyzer, which is able to track cells within microscopy images with minimal input required from the user. The program of cell analyzer was written in Python utilizing the open source computer vision (OpenCV) library and featured a graphical user interface that makes it easy for users to access. The functions of all codes were verified through closeness, area, centroid, contrast, variance, and cell tracking test. Cell analyzer primarily utilizes image preprocessing and edge detection techniques to isolate cell boundaries for detection and analysis. It uniquely recorded the area, displacement, speed, size, and direction of detected cell objects and visualized the data collected automatically for fast analysis. Our cell analyzer provides an easy-to-use tool through a graphical user interface for tracking cell motion and analyzing quantitative cell images.
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Affiliation(s)
- Lillian McAfee
- Department of Mechanical, Civil, and Biomedical Engineering, George Fox University, Newberg, Oregon, USA
| | - Zach Heath
- Department of Computer science, George Fox University, Newberg, Oregon, USA
| | - William Anderson
- Department of Mechanical, Civil, and Biomedical Engineering, George Fox University, Newberg, Oregon, USA
| | - Marvin Hozi
- Department of Computer science, George Fox University, Newberg, Oregon, USA
| | - John Walker Orr
- Department of Computer science, George Fox University, Newberg, Oregon, USA
| | - Youngbok Abraham Kang
- Department of Mechanical, Civil, and Biomedical Engineering, George Fox University, Newberg, Oregon, USA
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Holme B, Bjørnerud B, Pedersen NM, de la Ballina LR, Wesche J, Haugsten EM. Automated tracking of cell migration in phase contrast images with CellTraxx. Sci Rep 2023; 13:22982. [PMID: 38151514 PMCID: PMC10752880 DOI: 10.1038/s41598-023-50227-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2023] [Accepted: 12/17/2023] [Indexed: 12/29/2023] Open
Abstract
The ability of cells to move and migrate is required during development, but also in the adult in processes such as wound healing and immune responses. In addition, cancer cells exploit the cells' ability to migrate and invade to spread into nearby tissue and eventually metastasize. The majority of cancer deaths are caused by metastasis and the process of cell migration is therefore intensively studied. A common way to study cell migration is to observe cells through an optical microscope and record their movements over time. However, segmenting and tracking moving cells in phase contrast time-lapse video sequences is a challenging task. Several tools to track the velocity of migrating cells have been developed. Unfortunately, most of the automated tools are made for fluorescence images even though unlabelled cells are often preferred to avoid phototoxicity. Consequently, researchers are constrained with laborious manual tracking tools using ImageJ or similar software. We have therefore developed a freely available, user-friendly, automated tracking tool called CellTraxx. This software makes it easy to measure the velocity and directness of migrating cells in phase contrast images. Here, we demonstrate that our tool efficiently recognizes and tracks unlabelled cells of different morphologies and sizes (HeLa, RPE1, MDA-MB-231, HT1080, U2OS, PC-3) in several types of cell migration assays (random migration, wound healing and cells embedded in collagen). We also provide a detailed protocol and download instructions for CellTraxx.
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Affiliation(s)
- Børge Holme
- SINTEF Industry, Forskningsveien 1, 0373, Oslo, Norway
| | - Birgitte Bjørnerud
- Department of Tumor Biology, Institute for Cancer Research, The Norwegian Radium Hospital, Oslo University Hospital, Montebello, 0379, Oslo, Norway
- Centre for Cancer Cell Reprogramming, Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Montebello, 0379, Oslo, Norway
| | - Nina Marie Pedersen
- Centre for Cancer Cell Reprogramming, Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Montebello, 0379, Oslo, Norway
- Department of Molecular Cell Biology, Institute for Cancer Research, The Norwegian Radium Hospital, Oslo University Hospital, Montebello, 0379, Oslo, Norway
- Department of Nursing, Health and Laboratory Science, Faculty of Health, Welfare and Organisation, Østfold University College, PB 700, NO-1757, Halden, Norway
| | - Laura Rodriguez de la Ballina
- Centre for Cancer Cell Reprogramming, Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Montebello, 0379, Oslo, Norway
- Department of Molecular Cell Biology, Institute for Cancer Research, The Norwegian Radium Hospital, Oslo University Hospital, Montebello, 0379, Oslo, Norway
| | - Jørgen Wesche
- Department of Tumor Biology, Institute for Cancer Research, The Norwegian Radium Hospital, Oslo University Hospital, Montebello, 0379, Oslo, Norway
- Centre for Cancer Cell Reprogramming, Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Montebello, 0379, Oslo, Norway
- Department of Molecular Medicine, Institute of Basic Medical Sciences, University of Oslo, 0372, Oslo, Norway
| | - Ellen Margrethe Haugsten
- Department of Tumor Biology, Institute for Cancer Research, The Norwegian Radium Hospital, Oslo University Hospital, Montebello, 0379, Oslo, Norway.
- Centre for Cancer Cell Reprogramming, Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Montebello, 0379, Oslo, Norway.
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Löffler K, Scherr T, Mikut R. A graph-based cell tracking algorithm with few manually tunable parameters and automated segmentation error correction. PLoS One 2021; 16:e0249257. [PMID: 34492015 PMCID: PMC8423278 DOI: 10.1371/journal.pone.0249257] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2021] [Accepted: 08/03/2021] [Indexed: 11/29/2022] Open
Abstract
Automatic cell segmentation and tracking enables to gain quantitative insights into the processes driving cell migration. To investigate new data with minimal manual effort, cell tracking algorithms should be easy to apply and reduce manual curation time by providing automatic correction of segmentation errors. Current cell tracking algorithms, however, are either easy to apply to new data sets but lack automatic segmentation error correction, or have a vast set of parameters that needs either manual tuning or annotated data for parameter tuning. In this work, we propose a tracking algorithm with only few manually tunable parameters and automatic segmentation error correction. Moreover, no training data is needed. We compare the performance of our approach to three well-performing tracking algorithms from the Cell Tracking Challenge on data sets with simulated, degraded segmentation—including false negatives, over- and under-segmentation errors. Our tracking algorithm can correct false negatives, over- and under-segmentation errors as well as a mixture of the aforementioned segmentation errors. On data sets with under-segmentation errors or a mixture of segmentation errors our approach performs best. Moreover, without requiring additional manual tuning, our approach ranks several times in the top 3 on the 6th edition of the Cell Tracking Challenge.
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Affiliation(s)
- Katharina Löffler
- Institute for Automation and Applied Informatics, Karlsruhe Institute of Technology, Eggenstein-Leopoldshafen, Germany
- Institute of Biological and Chemical Systems - Biological Information Processing, Karlsruhe Institute of Technology, Eggenstein-Leopoldshafen, Germany
- * E-mail:
| | - Tim Scherr
- Institute for Automation and Applied Informatics, Karlsruhe Institute of Technology, Eggenstein-Leopoldshafen, Germany
| | - Ralf Mikut
- Institute for Automation and Applied Informatics, Karlsruhe Institute of Technology, Eggenstein-Leopoldshafen, Germany
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