Automated analysis of phase-contrast optical microscopy time-lapse images: application to wound healing and cell motility assays of breast cancer

Schematic of the chapter's end-to-end workflow, drawn from the publisher summary. This is a schematic, not the chapter's figure.

Chapter summary

This chapter describes a workflow for analyzing phase-contrast microscopy (PCM) data from two fundamental types of biomedical assays: assays for cell motility and assays for wound healing. The workflow of the analysis is composed of the methods for acquiring, restoring, segmenting, and quantifying biomedical data. In the literature, there have been separate methods aimed at specific stages of PCM data analysis. Nonetheless, there has never been a complete workflow for all stages of analysis. This work is an innovation that proposes an end-to-end workflow for image pre-processing, deep learning segmentation, tracking, and quantification stages in cell motility and wound healing assay analyses. The findings indicate that domain knowledge can be used to make simple but significant improvements to the results of cutting-edge methods. Furthermore, even for deep learning-based methods, pre-processing is clearly a necessary step in the workflow.

Framework

Acquire and restore

Phase-contrast microscopy data from motility and wound healing assays is acquired and restored.

Segment

Deep learning segmentation isolates cells in the time-lapse images.

Track and quantify

Cell tracking and quantification measure motility and wound healing over time.

Domain knowledge

Simple, domain-informed improvements lift the results of cutting-edge methods, and pre-processing proves necessary even for deep learning.

Resources

Chapter DOI: 10.1016/B978-0-323-96129-5.00013-5
Book: ScienceDirect book page

BibTeX

@incollection{erdem2023automated,
  title = {Automated analysis of phase-contrast optical microscopy time-lapse images: application to wound healing and cell motility assays of breast cancer},
  author = {Erdem, Yusuf Sait and Ayanzadeh, Aydin and Mayal{\i}, Berkay and Bal{\i}k{\c{c}}i, Muhammed and Belli, {\"O}zge Nur and U{\c{c}}ar, Mahmut and Yal{\c{c}}{\i}n {\"O}zyusal, {\"O}zden and Pesen Okvur, Devrim and {\"O}nal, Sevgi and Morani, Kenan and Iheme, Leonardo Obinna and T{\"o}reyin, Beh{\c{c}}et U{\u{g}}ur and {\"U}nay, Devrim},
  booktitle = {Diagnostic Biomedical Signal and Image Processing Applications with Deep Learning Methods},
  editor = {Polat, Kemal and {\"O}zt{\"u}rk, Saban},
  publisher = {Elsevier / Academic Press},
  pages = {137--154},
  year = {2023},
  doi = {10.1016/B978-0-323-96129-5.00013-5},
  url = {https://doi.org/10.1016/B978-0-323-96129-5.00013-5}
}