Artificial Neural Networks for Automated Cell Quantification in Lensless LED Imaging Systems
Agus Budi Dharmawan,
Gregor Scholz,
Shinta Mariana,
Philipp Hörmann,
Igi Ardiyanto,
Sunu Wibirama,
Jana Hartmann,
Joan Daniel Prades,
Karsten Hiller,
Andreas Waag,
Hutomo Suryo Wasisto
Affiliations
Agus Budi Dharmawan
Institute of Semiconductor Technology (IHT) and Laboratory for Emerging Nanometrology (LENA), Technische Universität Braunschweig, 38106 Braunschweig, Germany
Gregor Scholz
Institute of Semiconductor Technology (IHT) and Laboratory for Emerging Nanometrology (LENA), Technische Universität Braunschweig, 38106 Braunschweig, Germany
Shinta Mariana
Institute of Semiconductor Technology (IHT) and Laboratory for Emerging Nanometrology (LENA), Technische Universität Braunschweig, 38106 Braunschweig, Germany
Philipp Hörmann
Institute for Biochemistry, Biotechnology and Bioinformatics, Technische Universität Braunschweig, 38106 Braunschweig, Germany
Igi Ardiyanto
Department of Electrical Engineering and Information Technology (DTETI), Universitas Gadjah Mada, Yogyakarta 55281, Indonesia
Sunu Wibirama
Department of Electrical Engineering and Information Technology (DTETI), Universitas Gadjah Mada, Yogyakarta 55281, Indonesia
Jana Hartmann
Institute of Semiconductor Technology (IHT) and Laboratory for Emerging Nanometrology (LENA), Technische Universität Braunschweig, 38106 Braunschweig, Germany
Joan Daniel Prades
MIND-IN2UB, Department of Electronic and Biomedical Engineering, University of Barcelona, 08028 Barcelona, Spain
Karsten Hiller
Institute for Biochemistry, Biotechnology and Bioinformatics, Technische Universität Braunschweig, 38106 Braunschweig, Germany
Andreas Waag
Institute of Semiconductor Technology (IHT) and Laboratory for Emerging Nanometrology (LENA), Technische Universität Braunschweig, 38106 Braunschweig, Germany
Hutomo Suryo Wasisto
Institute of Semiconductor Technology (IHT) and Laboratory for Emerging Nanometrology (LENA), Technische Universität Braunschweig, 38106 Braunschweig, Germany
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.
