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Deep learning democratizes nano-scale imaging

17 Dec '18 | By Maxim Batalin
Deep learning democratizes nano-scale imaging
The technique transforms low-resolution images from a fluorescence microscope (a) into super-resolution images (b) that compare favorably with those from high-resolution equipment (c). Images show sub-cellular proteins within a cell, and different panels correspond to different observation times. Credit: Ozcan Lab at UCLA.

Many problems in physical and biological sciences as well as engineering rely on our ability to monitor objects or processes at nano-scale, and fluorescence microscopy has been used for decades as one of our most useful information sources, leading to various discoveries about the inner workings of nano-scale processes, for example at the sub-cellular level. Imaging of such nano-scale objects often requires rather expensive and delicate instrumentation, also known as nanoscopy tools, which can only be accessed by professionals in well-resourced labs.

To democratize access to high-resolution fluorescence imaging and be able to resolve and monitor objects at nano-scale, UCLA researchers have developed a new method, based on artificial intelligence, to digitally transform fluorescence images acquired using a lower resolution and simpler microscope into images that match the resolution and quality of higher resolution and advanced microscopes that are built for nano-scale imaging. To achieve this transformation, an artificial neural network is trained by thousands of image pairs (lower resolution vs. higher resolution images of the same samples), teaching the deep neural network the cross-modality image transformation from a much simpler and cheaper microscope into a high-end nanoscope. Once the training is complete, the deep neural network can blindly take in an image of the lower resolution and simpler microscope to digitally super-resolve the features of the nanoscopic objects in the sample, matching the performance of a much more advanced nanoscopy instrument.  

This work was published in Nature Methods, a journal of the Springer Nature Publishing Group. This research was led by Dr. Aydogan Ozcan, an associate director of the UCLA California NanoSystems Institute (CNSI) and the Chancellor’s Professor of electrical and computer engineering at the UCLA Henry Samueli School of Engineering and Applied Science. Hongda Wang, a UCLA graduate student, and Yair Rivenson, a UCLA postdoctoral scholar, are the study’s co-first authors.

This nanoscopic image transformation framework builds bridges across different imaging modalities and instruments, and its success was demonstrated by super-resolving various biological cells and tissue samples, matching the imaging resolution of much more advanced fluorescence nanoscopy tools using much simpler and more accessible microscopes. Furthermore, this technique allows imaging of dynamic events at nanoscale over a much larger sample volume, while also reducing the toxic effects of illumination photons on living organisms and cells.

“Our work demonstrates a significant step forward in computational microscopy, which might help to democratize super-resolution imaging by enabling new biological observations at nano-scale beyond well-equipped laboratories and institutions,” said Ozcan.

Other members of the research team were Yiyin Jin, Zhensong Wei, Ronald Gao, Harun Günaydin, members of the Ozcan Research Lab at UCLA, as well as Dr. Laurent A. Bentolila, the Director of CNSI Advanced Microscopy Facility at UCLA and Dr. Comert Kural, an assistant professor at the department of physics at the Ohio State University.

Ozcan lab is supported by NSF, HHMI and Koc Group. Imaging experiments were performed at the Advanced Light Microscopy/Spectroscopy Laboratory at CNSI and at the Advanced Imaging Center at Janelia Research Campus.


Link to the publication:  https://rdcu.be/bdG8c 

http://innovate.ee.ucla.edu/welcome.html

http://org.ee.ucla.edu/
Area of application: Dentistry , Dermatology, Gastroenterology, Gynecology and Obstetrics, Human genetics, Infectious disease and antibiotic resistance, Internal medicine and general medicine, Laboratory and environmental medicine, Neurology, Neurosurgery, Oncology, Ophthalmology Diagnostics and Imaging, Otorhinolaryngology, Pathology, Pediatrics and Neonatology, Pharmacology, Pulmonology, Reproductive Medicine, Rheumatology, Surgery, Urology and Nephrology, Medicine, other applications, Cellular biotechnology, Drug delivery, Molecular diagnostics, Pharmaceuticals (development, production, monitoring), Therapeutics, Tissue enginieering, Health, other applications, Biochemistry, Cell Biology, Developmental biology, Ecology, Genetics, Human biology, Microbiology, Molecular biology, Physiology, Biology, other applications, Other Areas of Application
Methods and Techniques: Endoscopy, General microscopy (white light, confocal, bright field, dark field, phase contrast, DIC etc.), Linear and non-linear fluorescence imaging (confocal LSM, multi-photon, STED, PALM, STORM, SIM, FRET, FRAP, FLIM, etc.), Linear and non-linear vibrational microscopy / imaging (IR, confocal Raman, CARS, SRS etc.), Near-field microscopy (SNOM, AFM, STM, etc.), Optical Coherence Tomography (OCT ), Operating microscopy, Photoacoustic imaging (PAI, MSOT), Polarimetry, Probe and sensor development, Terahertz imaging, Thermography, Microscopy/Imaging, other methods and techniques, Biochips, Bioassays, High-throughput screening, Micro-array technologies, Point-of-care, other methods and techniques, Big data, Chemometrics, Image analysis, Image processing, Digitalization, other methods and techniques, Enabling Technologies, other methods and techniques, Other Methods and Techniques

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