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Dergiler / Natural and Applied Sciences Journal / 2021 / Cilt: 4 - Sayı: 1

An Image Segmentation Method for Wound Healing Assay Images

Yusuf Sait ERDEM
Özden YALÇIN ÖZUYSAL
Devrim PESEN OKVUR
Behçet TÖREYİN
Devrim ÜNAY
Sayfa
30–37
Yayın Tarihi
2021-06-30
DOI
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Yapay Zeka Hızlı Bakış & Çıkarımlar

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⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

In this study we have proposed a novel method to overcome these difficulties.

📌 3 Maddede Bu Makale

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Amaç & Hipotez:Wound healing assays are important for molecular biologists to understand the mechanisms of cell migration.
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Temel Bulgular & Veri:For the analysis of wound healing assays, accurate segmentation of the wound front is a necessity.
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Sonuç & Katkı:In this study we have proposed a novel method to overcome these difficulties.
🔬Tür:Araştırma Makalesi
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:Image segmentation · quantification · wound healing assay

Abstract

Wound healing assays are important for molecular biologists to understand the mechanisms of cell migration. For the analysis of wound healing assays, accurate segmentation of the wound front is a necessity. Manual annotation of the wound front is inconvenient since it is time-consuming and annotator-dependent. Thus automated, fast, and robust solutions are required. There are several image processing techniques proposed to fulfill this need. However, requirement for specification of optimal parameters, the need for human intervention, and the lack of high accuracy emerge as the downfalls for most of them. In this study we have proposed a novel method to overcome these difficulties.

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Bu Makaleye Atıf Yap

ERDEM, Y. S., ÖZUYSAL, Ö. Y., OKVUR, D. P., TÖREYİN, B., ÜNAY, D. (2021). An Image Segmentation Method for Wound Healing Assay Images. Natural and Applied Sciences Journal, Cilt: 4 - Sayı: 1, 30–37. https://ajindex.com/articles/793067

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Kaynakça (7)

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  2. Suarez-Arnedo, Alejandra, et al. "An image J plugin for the high throughput image analysis of in vitro scratch wound healing assays." bioRxiv (2020).
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  5. Grada, Ayman, et al. "Research techniques made simple: analysis of collective cell migration using the wound healing assay." Journal of Investigative Dermatology 137.2 (2017): e11-e16. Huang, Kai, and Robert F. Murphy. "From quantitative microscopy to automated image understanding." Journal of biomedical optics 9.5 (2004): 893-913.
  6. Garcia, Fossa, Vladimir Fernanda Gaal, and B. de Jesus Marcelo. "PyScratch: an ease of use tool for analysis of Scratch assays." Computer Methods and Programs in Biomedicine (2020): 105476. Milde, Florian, et al. "Cell Image Velocimetry (CIV): boosting the automated quantification of cell migration in wound healing assays." Integrative Biology 4.11 (2012): 1437-1447. Wound healing image segmentation tool: http://dev.mri.cnrs.fr/projects/imagej-macros/wiki/Wound_Healing_Tool
  7. Mayalı, Berkay, et al. "Automated Analysis of Wound Healing Microscopy Image Series-A Preliminary Study." 2020 Medical Technologies Congress (TIPTEKNO). IEEE, 2020. Image annotation online tool: https://supervise.ly