Robust optical flow algorithm for general single cell segmentation

• Published in: PloS one Vol. 17; no. 1; p. e0261763
• Main Authors: Robitaille, Michael C., Byers, Jeff M., Christodoulides, Joseph A., Raphael, Marc P.
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 14.01.2022; Public Library of Science (PLoS)
• Subjects: Algorithms; Analysis; Biology and Life Sciences; Cell migration; Cell morphology; Cell separation; Cells; Computer and Information Sciences; Cytology; Datasets; Deep learning; Evaluation; Image Processing, Computer-Assisted; Image segmentation; Labels; Laboratories; Learning algorithms; Machine learning; Machine vision; Materials science; Medicine and Health Sciences; Microscopy; Optical flow (image analysis); Optimization; Phenotypes; Physical Sciences; Research and Analysis Methods; Robustness; Single-Cell Analysis; Social Sciences; Software; Structure; United States; United States > US
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0261763

Cell segmentation is crucial to the field of cell biology, as the accurate extraction of single-cell morphology, migration, and ultimately behavior from time-lapse live cell imagery are of paramount importance to elucidate and understand basic cellular processes. In an effort to increase available segmentation tools that can perform across research groups and platforms, we introduce a novel segmentation approach centered around optical flow and show that it achieves robust segmentation of single cells by validating it on multiple cell types, phenotypes, optical modalities, and in-vitro environments with or without labels. By leveraging cell movement in time-lapse imagery as a means to distinguish cells from their background and augmenting the output with machine vision operations, our algorithm reduces the number of adjustable parameters needed for manual optimization to two. We show that this approach offers the advantage of quicker processing times compared to contemporary machine learning based methods that require manual labeling for training, and in most cases achieves higher quality segmentation as well. This algorithm is packaged within MATLAB, offering an accessible means for general cell segmentation in a time-efficient manner.