Artificial Neural Networks for Automated Cell Quantification in Lensless LED Imaging Systems

Proceedings

Wibirama, Sunu¹  Hörmann, Philipp²  Scholz, Gregor³  Mariana, Shinta⁴  Dharmawan, Agus Budi⁵  Ardiyanto, Igi⁶  Hartmann, Jana⁷ 

[1] Authors to whom correspondence should be addressed.;Department of Electrical Engineering and Information Technology (DTETI), Universitas Gadjah Mada, Yogyakarta 55281, Indonesia;Faculty of Information Technology, Universitas Tarumanagara, Jakarta 11440, Indonesia;Institute for Biochemistry, Biotechnology and Bioinformatics, Technische Universität Braunschweig, 38106 Braunschweig, Germany;Institute of Semiconductor Technology (IHT) and Laboratory for Emerging Nanometrology (LENA), Technische Universität Braunschweig, 38106 Braunschweig, Germany;MIND-IN2UB, Department of Electronic and Biomedical Engineering, University of Barcelona, 08028 Barcelona, Spain;Presented at the Eurosensors 2018 Conference, Graz, Austria, 9–12 September 2018

关键词: artificial neural networks;    cell counting;    lensless holographic microscopy;    principal component analysis;   

DOI  :  10.3390/proceedings2130989

学科分类：社会科学、人文和艺术（综合）

来源: mdpi

【 摘 要 】

Cell registration by artificial neural networks (ANNs) in combination with principal component analysis (PCA) has been demonstrated for cell images acquired by light emitting diode (LED)-based compact holographic microscopy. In this approach, principal component analysis was used to find the feature values from cells and background, which would be subsequently employed as neural inputs into the artificial neural networks. Image datasets were acquired from multiple cell cultures using a lensless microscope, where the reference data was generated by a manually analyzed recording. To evaluate the developed automatic cell counter, the trained system was assessed on different data sets to detect immortalized mouse astrocytes, exhibiting a detection accuracy of ~81% compared with manual analysis. The results show that the feature values from principal component analysis and feature learning by artificial neural networks are able to provide an automatic approach on the cell detection and registration in lensless holographic imaging.

【 授权许可】

CC BY