Methodology for comprehensive cell-level analysis of wound healing experiments using deep learning in MATLAB
Open accessAbstract
BACKGROUND: Endothelial healing after deployment of cardiovascular devices is particularly important in the context of clinical outcome. It is therefore of great interest to develop tools for a precise prediction of endothelial growth after injury in the process of implant deployment. For experimental investigation of re-endothelialization in vitro cell migration assays are routinely used. However, semi-automatic analyses of live cell images are often based on gray value distributions and are as such limited by image quality and user dependence. The rise of deep learning algorithms offers promising opportunities for application in medical image analysis. Here, we present an intelligent cell detection (iCD) approach for comprehensive assay analysis to obtain essential characteristics on cell and population scale. RESULTS: In an in vitro wound healing assay, we compared conventional analysis methods with our iCD approach. Therefore we determined cell density and cell velocity on cell scale and the movement of the cell layer as well as the gap closure between two cell monolayers on population scale. Our data demonstrate that cell density analysis based on deep learning algorithms is superior to an adaptive threshold method regarding robustness against image distortion. In addition, results on cell scale obtained with iCD are in agreement with manually velocity detection, while conventional methods, such as Cell Image Velocimetry (CIV), underestimate cell velocity by a factor of 0.5. Further, we found that iCD analysis of the monolayer movement gave results just as well as manual freehand detection, while conventional methods again shows more frayed leading edge detection compared to manual detection. Analysis of monolayer edge protrusion by ICD also produced results, which are close to manual estimation with an relative error of 11.7%. In comparison, the conventional Canny method gave a relative error of 76.4%. CONCLUSION: The results of our experiments indicate that deep learning algorithms such as our iCD have the ability to outperform conventional methods in the field of wound healing analysis. The combined analysis on cell and population scale using iCD is very well suited for timesaving and high quality wound healing analysis enabling the research community to gain detailed understanding of endothelial movement.
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The reliability and reproducibility of the wound healing assay can be severely impacted by many factors if they are not adequately controlled.
“several articles highlight an increasing number of factors that can severely impact the reliability and reproducibility of the assay, if they are not adequately controlled [38,39,40,41,42]”
From Optimized Wound Healing Assay to Study Extracellular Vesicle-Driven Glioblastoma Cell Migration (2026) · cites this paper as [39]
Protocols involving extracellular vesicles and glioblastoma cells are particularly poorly validated.
“with protocols involving EVs and GB cells being particularly poorly validated.”
From Optimized Wound Healing Assay to Study Extracellular Vesicle-Driven Glioblastoma Cell Migration (2026) · cites this paper as [39]
The Methods (Wound healing experiments; page unavailable) describe assays using human coronary artery endothelial cells, while the Conclusions discuss limitations of wound-healing image analysis. A Discussion passage mentions glioblastoma cells only as a segmentation benchmark. Searches for extracellular vesicles and poorly validated EV or glioblastoma protocols found no passage addressing the claim. (LLM, conf=0.76)
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- Authors
- Oldenburg, J., Maletzki, L., Strohbach, A., Bellé, P., Siewert, S., Busch, R., Felix, S. B., Schmitz, K.-P., & Stiehm, M.
- Affiliations
- Institute for ImplantTechnology and Biomaterials (DE)
- Universitätsmedizin Greifswald (DE)
- Corresponding author
- Oldenburg, Jan
- ORCID
- Venue
- BMC Molecular and Cell Biology
- Published
- 2021-06-02
- Volume
- 22
- Issue
- 1
- Pages
- 32-32
- ISSN
- 2661-8850 (ISSN-L)
- PMC
- PMC8170781
- OpenAlex
- W3165336686
- Cited by
- 16
- References
- 40
- Citations by year
- 2026: 2 · 2025: 3 · 2024: 7 · 2023: 2 · 2022: 1 · 2021: 1
- FWCI
- 1.25
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- BMC Molecular and Cell Biology (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- European Social Fund (ESF) and the Ministry of Education, Science and Culture of Mecklenburg-Western Pomerania (ESF/14-BM-A55-0044/16)
- Universitätsmedizin Rostock
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Biophysics › Cell Image Analysis Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Digital Imaging for Blood Diseases
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Digital Image Processing Techniques
- Keywords
- wound healing assay, cell migration, cell detection, deep learning, cell velocity, cell density, gap closure, adaptive thresholding
- MeSH terms
- Deep Learning, Endothelium, Vascular, Humans, Wound Healing, Cell Tracking
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