Figure - PMC

Skip to main content

An official website of the United States government

Here's how you know

Here's how you know

Official websites use .gov
A .gov website belongs to an official government organization in the United States.

Secure .gov websites use HTTPS
A lock ( ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites.

View full-text article in PMC

. 2017 Sep 29;7:12454. doi: 10.1038/s41598-017-12378-4

Search in PMC
Search in PubMed
View in NLM Catalog
Add to search

© The Author(s) 2017

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

PMC Copyright notice

Workflow of the label-free detection of drug-induced morphological variations in cancer cells with optofluidic time-stretch microscopy. The insets show the schematics of optofluidic time-stretch microscopy and machine-learning-aided image analysis. As schematically shown in the figure, the procedure of our method can be divided into three parts: (i) cell culturing and treatment, (ii) optofluidic time-stretch imaging, and (iii) machine-learning-aided image analysis. In the first part, cells of interest are cultured and treated by a drug. In the second part, the treated cells are subject to high-throughput bright-field imaging. In the last part, machine learning algorithm is applied to the images for the identification of their morphological variations induced by drug treatment. While the morphological change in a single cell is miniscule, the large number of bright-field single-cell images can render the morphological change discovered by machine learning statistically significant and robust.