Morphologically Based Cell Classification in Mixed Cultures
10.16156/j.1004-7220.2019.02.07
- VernacularTitle:一种对混养细胞基于形态的细胞分选方法
- Author:
Kaiqiang LIU
1
;
Mengjiao HUA
1
;
Nan LIN
2
;
Yu WU
3
,
4
,
5
Author Information
1. Department of Engineering Mechanics, Zhejiang University
2. College of Life Sciences, Zhejiang University
3. Department of Engineering Mechanics, School of Aeronautics and Astronautics, Zhejiang University
4. Key Laboratory of Soft Machines and Smart Devices of Zhejiang Province, Zhejiang University
5. Soft Matter Research Center, Zhejiang University
- Publication Type:Journal Article
- Keywords:
cell culture;
immunofluorescence staining;
image acquisition and processing;
machine learning
- From:
Journal of Medical Biomechanics
2019;34(2):E153-E159
- CountryChina
- Language:Chinese
-
Abstract:
Objective To make quantitative analysis on collected cell images combined with machine learning integrated clustering algorithm, so as to explore a method for fast recognition and classification of cells in mixed cultures based on morphology. Methods The morphometric properties of A549 and 3T3 cells in vitro were characterized by immunostaining, the fluorescent images were then analyzed with CellProfiler to extract the parameters of cell morphology. The parameters were loaded into CellProfiler Analyst to be trained with machine learning algorithm, and a rule was developed to form a generalization capability for cell classification in mixed cultures. Results The accuracy of the training classifier was 81-24%, and the binary classifications of A549 and 3T3 cells could be realized. Conclusions The method of machine learning is very effective in parameter clustering. The application of machine learning into cell image recognition can provide pre-judgment for rapid pathological examination of tissue sections, thereby reducing the workload of doctors and improving the accuracy of diagnosis.
