Application of Support Vector Machine to Recognize Trans-differentiated Neural Progenitor Cells for Bright-field Microscopy
One possible solution of the investigation of the cell fate decision and its function is the study of cell morphology.Bright-field imaging analysis allow us to use a labeling free and non-invasive approach to measure the morphological dynamics during cellular reprogramming,which includes induced pluripotent stem cells (iPSCs),and trans-differentiated neural progenitor cells (NPCs) from somatic cell source.In order to automatically analyze and cultivate cells,a system classifying NPCs under bright-field microscopic imaging is necessary.In this paper,we investigate the use of support vector machine (SVM) based on a set of features for this task.The results illustrate that such a data driven approach has remarkable recognition and generalization performance.
machine learning support vector machine trans-differentiated neural progenitor cells cell recognition bright-field microscopy
Bo Jiang Xinyuan Wang Qunxia Gao Ziqi Lin Rui Zhang Xiao Zhang
Guangzhou Institute of Biomedicine and Health Chinese Academy of Sciences, Guangzhou, Science Park
国际会议
秦皇岛
英文
215-219
2015-09-18（万方平台首次上网日期，不代表论文的发表时间）