Deep learning-based hologram generation using a white light source

Digital holographic microscopy enables the recording of sample holograms which contain 3D volumetric information. However, additional optical elements, such as partially or fully coherent light source and a pinhole, are required to induce diffraction and interference. Here, we present a deep neural network based on generative adversarial network (GAN) to perform image transformation from a defocused bright-field (BF) image acquired from a general white light source to a holographic image. Training image pairs of 11,050 for image conversion were gathered by using a hybrid BF and hologram imaging technique. The performance of the trained network was evaluated by comparing generated and ground truth holograms of microspheres and erythrocytes distributed in 3D. Holograms generated from BF images through the trained GAN showed enhanced image contrast with 3–5 times increased signal-to-noise ratio compared to ground truth holograms and provided 3D positional information and light scattering patterns of the samples. The developed GAN-based method is a promising mean for dynamic analysis of microscale objects with providing detailed 3D positional information and monitoring biological samples precisely even though conventional BF microscopic setting is utilized.

[1]  Yong-Seok Choi,et al.  Advances in digital holographic micro-PTV for analyzing microscale flows , 2012 .

[2]  S. Vanapalli,et al.  Label-free, high-throughput holographic screening and enumeration of tumor cells in blood. , 2017, Lab on a chip.

[3]  C. Depeursinge,et al.  Quantitative phase imaging in biomedicine , 2012, 2012 Conference on Lasers and Electro-Optics (CLEO).

[4]  Taesik Go,et al.  Machine learning‐based in‐line holographic sensing of unstained malaria‐infected red blood cells , 2018, Journal of biophotonics.

[5]  J. Chi,et al.  Automated Detection of P. falciparum Using Machine Learning Algorithms with Quantitative Phase Images of Unstained Cells , 2016, PloS one.

[6]  Yong-Seok Choi,et al.  Three-dimensional volumetric measurement of red blood cell motion using digital holographic microscopy. , 2009, Applied optics.

[7]  Sang Joon Lee,et al.  Deep learning-based digital in-line holographic microscopy for high resolution with extended field of view , 2019, Optics & Laser Technology.

[8]  Yibo Zhang,et al.  Deep learning enhanced mobile-phone microscopy , 2017, ACS Photonics.

[9]  Hyun-seok Min,et al.  Quantitative Phase Imaging and Artificial Intelligence: A Review , 2018, IEEE Journal of Selected Topics in Quantum Electronics.

[10]  Bahram Jalali,et al.  High-throughput single-microparticle imaging flow analyzer , 2012, Proceedings of the National Academy of Sciences.

[11]  Aydogan Ozcan,et al.  High-throughput lens-free blood analysis on a chip. , 2010, Analytical chemistry.

[12]  Yong-Seok Choi,et al.  High-accuracy three-dimensional position measurement of tens of micrometers size transparent microspheres using digital in-line holographic microscopy. , 2011, Optics letters.

[13]  Van Lam,et al.  Automatic phase aberration compensation for digital holographic microscopy based on deep learning background detection. , 2017, Optics express.

[14]  YongKeun Park,et al.  Identification of non-activated lymphocytes using three-dimensional refractive index tomography and machine learning , 2017, Scientific Reports.

[15]  Sang Joon Lee,et al.  Label-free sensor for automatic identification of erythrocytes using digital in-line holographic microscopy and machine learning. , 2018, Biosensors & bioelectronics.

[16]  Yibo Zhang,et al.  Phase recovery and holographic image reconstruction using deep learning in neural networks , 2017, Light: Science & Applications.

[17]  Edmund Y. Lam,et al.  End-to-end deep learning framework for digital holographic reconstruction , 2019, Advanced Photonics.

[18]  Jonghee Yoon,et al.  Holographic deep learning for rapid optical screening of anthrax spores , 2017, Science Advances.

[19]  J. Katz,et al.  Applications of Holography in Fluid Mechanics and Particle Dynamics , 2010 .

[20]  Yann LeCun,et al.  Deep multi-scale video prediction beyond mean square error , 2015, ICLR.

[21]  Aydogan Ozcan,et al.  Label-free 3D computational imaging of spermatozoon locomotion, head spin and flagellum beating over a large volume , 2017, Light: Science & Applications.

[22]  Sang Joon Lee,et al.  Precise measurement of orientations of transparent ellipsoidal particles through digital holographic microscopy. , 2016, Optics express.

[23]  Derek Tseng,et al.  Compact, light-weight and cost-effective microscope based on lensless incoherent holography for telemedicine applications. , 2010, Lab on a chip.

[24]  Yu Zhao,et al.  SNR enhancement in in-line particle holography with the aid of off-axis illumination. , 2019, Optics express.

[25]  Yong-Seok Choi,et al.  Supplementary Material (esi) for Lab on a Chip Lateral and Cross-lateral Focusing of Spherical Particles in a Square Microchannel , 2022 .

[26]  N. Aikawa,et al.  Size-Based Differentiation of Cancer and Normal Cells by a Particle Size Analyzer Assisted by a Cell-Recognition PC Software. , 2018, Biological & pharmaceutical bulletin.

[27]  Sang Joon Lee,et al.  Focusing and alignment of erythrocytes in a viscoelastic medium , 2017, Scientific Reports.

[28]  Myung K. Kim,et al.  Wavelength-scanning digital interference holography for tomographic three-dimensional imaging by use of the angular spectrum method. , 2005, Optics letters.

[29]  Roman Stocker,et al.  Failed escape: solid surfaces prevent tumbling of Escherichia coli. , 2014, Physical review letters.

[30]  S. Lee,et al.  Hybrid bright-field and hologram imaging of cell dynamics , 2016, Scientific Reports.

[31]  Yibo Zhang,et al.  Extended depth-of-field in holographic image reconstruction using deep learning based auto-focusing and phase-recovery , 2018, Optica.

[32]  Aydogan Ozcan,et al.  Label-Free Bioaerosol Sensing Using Mobile Microscopy and Deep Learning , 2018, ACS Photonics.

[33]  Chandan Chakraborty,et al.  Machine learning approach for automated screening of malaria parasite using light microscopic images. , 2013, Micron.

[34]  H. Greenspan,et al.  Quantitative phase microscopy spatial signatures of cancer cells , 2017, Cytometry. Part A : the journal of the International Society for Analytical Cytology.

[35]  Bahram Javidi,et al.  Cell morphology-based classification of red blood cells using holographic imaging informatics. , 2016, Biomedical optics express.

[36]  Paul Babyn,et al.  Generative Adversarial Network in Medical Imaging: A Review , 2018, Medical Image Anal..

[37]  Aydogan Ozcan,et al.  Bright-field holography: cross-modality deep learning enables snapshot 3D imaging with bright-field contrast using a single hologram , 2018, Light: Science & Applications.

[38]  P Memmolo,et al.  Red blood cell as an adaptive optofluidic microlens , 2015, Nature Communications.

[39]  Yi-Ping Phoebe Chen,et al.  Cell morphology based classification for red cells in blood smear images , 2014, Pattern Recognit. Lett..

[40]  Zachary S. Ballard,et al.  Air quality monitoring using mobile microscopy and machine learning , 2017, Light: Science & Applications.

[41]  ChenYi-Ping Phoebe,et al.  Cell morphology based classification for red cells in blood smear images , 2014 .

[42]  Derek K. Tseng,et al.  Detection of waterborne parasites using field-portable and cost-effective lensfree microscopy. , 2010, Lab on a chip.

[43]  Tomi Pitkäaho,et al.  Focus prediction in digital holographic microscopy using deep convolutional neural networks. , 2019, Applied optics.

[44]  Pasquale Memmolo,et al.  Recent advances in holographic 3D particle tracking , 2015 .

[45]  Sang Joon Lee,et al.  Three-dimensional swimming motility of microorganism in the near-wall region , 2016 .

[46]  Yi Luo,et al.  Deep Learning Enables High-Throughput Analysis of Particle-Aggregation-Based Biosensors Imaged Using Holography , 2018, ACS Photonics.

[47]  Guigang Zhang,et al.  Deep Learning , 2016, Int. J. Semantic Comput..

[48]  Edmund Y. Lam,et al.  Learning-based nonparametric autofocusing for digital holography , 2018 .

[49]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[50]  Yoshua Bengio,et al.  Generative Adversarial Nets , 2014, NIPS.

[51]  Pasquale Memmolo,et al.  Investigation on dynamics of red blood cells through their behavior as biophotonic lenses , 2016, Journal of biomedical optics.

[52]  Xiangnan Wang,et al.  Fast phase retrieval in off-axis digital holographic microscopy through deep learning. , 2018, Optics express.

[53]  Gunho Choi,et al.  Cycle-consistent deep learning approach to coherent noise reduction in optical diffraction tomography. , 2018, Optics express.

[54]  Yibo Zhang,et al.  Deep learning-based super-resolution in coherent imaging systems , 2018, Scientific Reports.

[55]  Hayit Greenspan,et al.  Automated analysis of individual sperm cells using stain‐free interferometric phase microscopy and machine learning , 2017, Cytometry. Part A : the journal of the International Society for Analytical Cytology.

[56]  Donghyun You,et al.  Data-driven prediction of unsteady flow over a circular cylinder using deep learning , 2018, Journal of Fluid Mechanics.