Enstitümüz öğretim üyelerinden Prof. Dr. Behçet Uğur Töreyin'in de yazarları arasında bulunduğu "Automated analysis of phase-contrast optical microscopy time-lapse images: application to wound healing and cell motility assays of breast cancer" başlıklı kitap bölümü, Academic Press tarafından yayınlanan "Diagnostic Biomedical Signal and Image Processing Applications with Deep Learning Methods: A volume in Intelligent Data-Centric Systems" başlıklı kitap içerisinde yayınlanmıştır.

DOI (bölüm) https://doi.org/10.1016/B978-0-323-96129-5.00013-5
DOI (kitap) : https://doi.org/10.1016/C2021-0-02190-8

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.