1. <scp>Label‐free</scp> classification of dead and live colonic adenocarcinoma cells based on <scp>2D</scp> light scattering and deep learning analysis
- Author
-
Qi Xue, Ya Li, Shuaiyi Li, Jianning Yao, Xiaonan Yang, Jiayou Song, and Bing Chen
- Subjects
0301 basic medicine ,Histology ,Adenocarcinoma ,Biology ,Light scattering ,Pathology and Forensic Medicine ,03 medical and health sciences ,HT29 Cells ,Deep Learning ,0302 clinical medicine ,Humans ,Viability assay ,Colonic adenocarcinoma ,Label free ,Staining and Labeling ,Artificial neural network ,business.industry ,Deep learning ,Cell Biology ,Cell biology ,Cell staining ,030104 developmental biology ,030220 oncology & carcinogenesis ,Neural Networks, Computer ,Artificial intelligence ,business ,Algorithms - Abstract
The measurement of cell viability plays an essential role in the area of cell biology. At present, the common methods for cell viability assay mainly on the responses of cells to different dyes. However, the additional steps of cell staining will consequently cause time-consuming and laborious efforts. Furthermore, the process of cell staining is invasive and may cause internal structure damage of cells, restricting their reuse in subsequent experiments. In this work, we proposed a label-free method to classify live and dead colonic adenocarcinoma cells by 2D light scattering combined with the deep learning algorithm. The deep convolutional network of YOLO-v3 was used to identify and classify light scattering images of live and dead HT29 cells. This method achieved an excellent sensitivity (93.6%), specificity (94.4%), and accuracy (94%). The results showed that the combination of 2D light scattering images and deep neural network may provide a new label-free method for cellular analysis.
- Published
- 2021