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Generative Adversarial Networks Enable Cross-Modality Super-Resolution in Fluorescence Microscopy

Microscopy and Microanalysis 2019年第S2期25卷 1228-1229页

作者： Hongda Wang Yair Rivenson Yiyin Jin Zhensong Wei Ronald Gao Harun Günaydın Laurent A. Bentolila Comert Kural Aydogan Ozcan Electrical & Computer Engineering Department University of California Los Angeles Los Angeles USA Bioengineering Department University of California Los Angeles Los Angeles USA California NanoSystems Institute University of California Los Angeles Los Angeles USA Computer Science Department University of California Los Angeles Los Angeles USA Department of Chemistry and Biochemistry University of California Los Angeles Los Angeles USA Department of Physics Ohio State University Columbus USA Biophysics Graduate Program Ohio State University Columbus USA Department of Surgery David Geffen School of Medicine University of California Los Angeles Los Angeles USA.

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Neuroprosthetics: Durable and Fatigue-Resistant Soft Peripheral Neuroprosthetics for In Vivo Bidirectional Signaling (Adv. Mater. 20/2021)

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Advanced Materials 2021年第20期33卷

作者： Hyunseon Seo Sang Ihn Han Kang-Il Song Duhwan Seong Kyungwoo Lee Sun Hong Kim Taesung Park Ja Hoon Koo Mikyung Shin Hyoung Won Baac Ok Kyu Park Soong Ju Oh Hyung-Seop Han Hojeong Jeon Yu-Chan Kim Dae-Hyeong Kim Taeghwan Hyeon Donghee Son School of Medicine Sungkyunkwan University Suwon 16419 Republic of Korea Center for Biomaterials Biomedical Research Institute Korea Institute of Science and Technology Seoul 02792 Republic of Korea Center for Nanoparticle Research Institute of Basic Science (IBS) Seoul 08826 Republic of Korea School of Chemical and Biological Engineering Seoul National University Seoul 08826 Republic of Korea Medical Device Development Center Daegu-Gyeongbuk Medical Innovation Foundation Daegu 41061 Republic of Korea Department of Electrical and Computer Engineering Sungkyunkwan University Suwon 16419 Republic of Korea Department of Electrical and Computer Engineering Inter-University Semiconductor Research Center Seoul National University Seoul 08826 Republic of Korea Department of Materials Science and Engineering Korea University Seoul 02841 Republic of Korea Interdisciplinary Program for Bioengineering Seoul National University Seoul 08826 Republic of Korea Department of Biomedical Engineering SKKU Institute for Convergence Sungkyunkwan University Suwon 16419 Republic of Korea Center for Neuroscience Imaging Research Institute for Basic Science (IBS) Suwon 16419 Republic of Korea Department of Superintelligence Engineering Sungkyunkwan University Suwon 16419 Republic of Korea

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QU-BraTS: MICCAI BraTS 2020 Challenge on Quantifying Uncertainty in Brain Tumor Segmentation - Analysis of Ranking Scores and Benchmarking Results

arXiv

引用

arXiv 2021年

作者： Mehta, Raghav Filos, Angelos Baid, Ujjwal Sako, Chiharu McKinley, Richard Rebsamen, Michael Dätwyler, Katrin Meier, Raphael Radojewski, Piotr Murugesan, Gowtham Krishnan Nalawade, Sahil Ganesh, Chandan Wagner, Ben Yu, Fang F. Fei, Baowei Madhuranthakam, Ananth J. Maldjian, Joseph A. Daza, Laura Gómez, Catalina Arbeláez, Pablo Dai, Chengliang Wang, Shuo Reynaud, Hadrien Mo, Yuanhan Angelini, Elsa Guo, Yike Bai, Wenjia Banerjee, Subhashis Pei, Linmin Murat, A.K. Rosas-González, Sarahi Zemmoura, Ilyess Tauber, Clovis Vu, Minh H. Nyholm, Tufve Löfstedt, Tommy Ballestar, Laura Mora Vilaplana, Veronica McHugh, Hugh Talou, Gonzalo Maso Wang, Alan Patel, Jay Chang, Ken Hoebel, Katharina Gidwani, Mishka Arun, Nishanth Gupta, Sharut Aggarwal, Mehak Singh, Praveer Gerstner, Elizabeth R. Kalpathy-Cramer, Jayashree Boutry, Nicolas Huard, Alexis Vidyaratne, Lasitha Rahman, Md Monibor Iftekharuddin, Khan M. Chazalon, Joseph Puybareau, Elodie Tochon, Guillaume Ma, Jun Cabezas, Mariano Llado, Xavier Oliver, Arnau Valencia, Liliana Valverde, Sergi Amian, Mehdi Soltaninejad, Mohammadreza Myronenko, Andriy Hatamizadeh, Ali Feng, Xue Dou, Quan Tustison, Nicholas Meyer, Craig Shah, Nisarg A. Talbar, Sanjay Weber, Marc-André Mahajan, Abhishek Jakab, Andras Wiest, Roland Fathallah-Shaykh, Hassan M. Nazeri, Arash Milchenko, Mikhail Marcus, Daniel Kotrotsou, Aikaterini Colen, Rivka Freymann, John Kirby, Justin Davatzikos, Christos Menze, Bjoern Bakas, Spyridon Gal, Yarin Arbel, Tal McGill University MontrealQC Canada Group University of Oxford Oxford United Kingdom University of Pennsylvania PhiladelphiaPA United States Department of Radiology Perelman School of Medicine The University of Pennsylvania PhiladelphiaPA United States Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States University Institute of Diagnostic and Interventional Neuroradiology University of Bern Inselspital Bern University Hospital Bern Switzerland Department of Radiology University of Texas Southwestern Medical Center DallasTX United States Department of Bioengineering University of Texas DallasTX United States Advanced Imaging Research Center University of Texas Southwestern Medical Center DallasTX United States Universidad de los Andes Bogotá Colombia Data Science Institute Imperial College London London United Kingdom NIHR Imperial BRC ITMAT Data Science Group Imperial College London London United Kingdom Department of Brain Sciences Imperial College London London United Kingdom Machine Intelligence Unit Indian Statistical Institute Kolkata India Department of CSE University of Calcutta Kolkata India Department of Information Technology Uppsala University Uppsala Sweden Department of Diagnostic Radiology The University of Pittsburgh Medical Center PittsburghPA United States UMR U1253 iBrain Université de Tours Inserm Tours France Department of Radiation Sciences Umeå University Umeå Sweden Department of Computing Science Umeå University Umeå Sweden Signal Theory and Communications Department Universitat Politècnica de Catalunya Barcelona Tech Barcelona Spain Faculty of Medical and Health Sciences University of Auckland Auckland New Zealand Radiology Department Auckland City Hospital Auckland New Zealand Auckland Bioengineering Institute University of Auckland New Zealand Athinoula A. Martinos Center for Biomedical Imaging Department of Radiology

Deep learning (DL) models have provided state-of-the-art performance in various medical imaging benchmarking challenges, including the Brain Tumor Segmentation (BraTS) challenges. However, the task of focal pathology multi-compartment segmentation (e.g., tumor and lesion sub-regions) is particularly challenging, and potential errors hinder translating DL models into clinical workflows. Quantifying the reliability of DL model predictions in the form of uncertainties could enable clinical review of the most uncertain regions, thereby building trust and paving the way toward clinical translation. Several uncertainty estimation methods have recently been introduced for DL medical image segmentation tasks. Developing scores to evaluate and compare the performance of uncertainty measures will assist the end-user in making more informed decisions. In this study, we explore and evaluate a score developed during the BraTS 2019 and BraTS 2020 task on uncertainty quantification (QU-BraTS) and designed to assess and rank uncertainty estimates for brain tumor multi-compartment segmentation. This score (1) rewards uncertainty estimates that produce high confidence in correct assertions and those that assign low confidence levels at incorrect assertions, and (2) penalizes uncertainty measures that lead to a higher percentage of under-confident correct assertions. We further benchmark the segmentation uncertainties generated by 14 independent participating teams of QU-BraTS 2020, all of which also participated in the main BraTS segmentation task. Overall, our findings confirm the importance and complementary value that uncertainty estimates provide to segmentation algorithms, highlighting the need for uncertainty quantification in medical image analyses. Finally, in favor of transparency and reproducibility, our evaluation code is made publicly available at https://***/RagMeh11/QU-BraTS. © 2021, CC BY.

关键词： Benchmarking

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Physics-based learned design: Optimized coded-illumination for quantitative phase imaging

arXiv

引用

arXiv 2018年

作者： Kellman, Michael R. Bostan, Emrah Repina, Nicole Waller, Laura Department of Electrical Engineering and Computer Sciences University of California BerkeleyCA94720 United States Graduate Program in Bioengineering University of California BerkeleyCA94720 United States

Coded-illumination can enable quantitative phase microscopy of transparent samples with minimal hardware requirements. Intensity images are captured with different source patterns and a non-linear phase retrieval optimization reconstructs the image. The non-linear nature of the processing makes optimizing the illumination pattern designs complicated. Traditional techniques for experimental design (e.g. condition number optimization, spectral analysis) consider only linear measurement formation models and linear reconstructions. Deep neural networks (DNNs) can efficiently represent the non-linear process and can be optimized over via training in an end-to-end framework. However, DNNs typically require a large amount of training examples and parameters to properly learn the phase retrieval process, without making use of the known physical models. Here, we aim to use both our knowledge of the physics and the power of machine learning together. We develop a new data-driven approach to optimizing coded-illumination patterns for a LED array microscope for a given phase reconstruction algorithm. Our method incorporates both the physics of the measurement scheme and the non-linearity of the reconstruction algorithm into the design problem. This enables efficient parameterization, which allows us to use only a small number of training examples to learn designs that generalize well in the experimental setting without retraining. We show experimental results for both a well-characterized phase target and mouse fibroblast cells using coded-illumination patterns optimized for a sparsity-based phase reconstruction algorithm. Our learned design results using 2 measurements demonstrate similar accuracy to Fourier Ptychography with 69 measurements. Copyright © 2018, The Authors. All rights reserved.

关键词： Statistics

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Author Correction: Greengenes2 unifies microbial data in a single reference tree

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Nature biotechnology 2024年第5期42卷 813页

作者： Daniel McDonald Yueyu Jiang Metin Balaban Kalen Cantrell Qiyun Zhu Antonio Gonzalez James T Morton Giorgia Nicolaou Donovan H Parks Søren M Karst Mads Albertsen Philip Hugenholtz Todd DeSantis Se Jin Song Andrew Bartko Aki S Havulinna Pekka Jousilahti Susan Cheng Michael Inouye Teemu Niiranen Mohit Jain Veikko Salomaa Leo Lahti Siavash Mirarab Rob Knight Department of Pediatrics University of California San Diego School of Medicine La Jolla CA USA. Department of Electrical and Computer Engineering University of California San Diego La Jolla CA USA. Bioinformatics and Systems Biology Program University of California San Diego La Jolla CA USA. Department of Computer Science and Engineering University of California San Diego La Jolla CA USA. School of Life Sciences Arizona State University Tempe AZ USA. Biodesign Center for Fundamental and Applied Microbiomics Arizona State University Tempe AZ USA. Biostatistics & Bioinformatics Branch Eunice Kennedy Shriver National Institute of Child Health and Human Development National Institutes of Health Bethesda MD USA. Halicioglu Data Science Institute University of California San Diego La Jolla CA USA. Australian Centre for Ecogenomics School of Chemistry and Molecular Biosciences The University of Queensland St Lucia Queensland Australia. Department of Obstetrics and Gynecology Columbia University New York NY USA. Department of Chemistry and Bioscience Aalborg University Aalborg Denmark. Department of Informatics Second Genome Brisbane CA USA. Center for Microbiome Innovation Jacobs School of Engineering University of California San Diego La Jolla CA USA. Finnish Institute for Health and Welfare Helsinki Finland. Institute for Molecular Medicine Finland FIMM-HiLIFE Helsinki Finland. Division of Cardiology Brigham and Women's Hospital Boston MA USA. Cedars-Sinai Medical Center Los Angeles CA USA. Cambridge Baker Systems Genomics Initiative Baker Heart and Diabetes Institute Melbourne Victoria Australia. Cambridge Baker Systems Genomics Initiative Department of Public Health and Primary Care University of Cambridge Cambridge UK. Division of Medicine Turku University Hospital and University of Turku Turku Finland. Sapient Bioanalytics LLC San Diego CA USA. Department of Computing University of Turku Turku Finland. Department of Pediatrics Uni

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A metasurface-inspired focusing collector for concentrated solar power applications

A metasurface-inspired focusing collector for concentrated s...

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Frontiers in Optics, FIO 2018

作者： Ding, Qing Barna, Shama Jacobs, Kyle Choubal, Aakash Mensing, Glennys Zhang, Zhong Yamada, Kaito Tirawat, Robert Kincaid, Nicholas Zhu, Guangdong Wendelin, Tim Guo, L. Jay Ferreira, Placid Toussaint, Kimani C. University of Illinois at Urbana-Champaign UrbanaIL61801 United States Department of Electrical and Computer Engineering University of Illinois at Urbana-Champaign UrbanaIL61801 United States Department of Mechanical Science and Engineering University of Illinois at Urbana-Champaign UrbanaIL61801 United States Department of Electrical Engineering and Computer Science University of Michigan Ann ArborMI48109-2102 United States Applied Physics Program University of Michigan Ann ArborMI48109 United States National Renewable Energy Lab. GoldenCO80401 United States Solar Dynamics LLC United States Department of Bioengineering University of Illinois at Urbana-Champaign UrbanaIL61801 United States

We present a simple metasurface-inspired planar focusing collector for concentrated solar power. Fabrication is achieved using two-photon lithography, and subsequent nanoimprint lithography tests for scalability. Opti... 详细信息

ISBN: (纸本)9781943580460

关键词： Solar energy

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Quantifying myelin in brain tissue using color spatial light interference microscopy (cSLIM)

arXiv

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arXiv 2020年

作者： Fanous, Michael Caputo, Megan P. Lee, Young Jae Rund, Laurie A. Best-Popescu, Catherine Kandel, Mikhail E. Johnson, Rodney W. Das, Tapas Kuchan, Matthew J. Popescu, Gabriel Quantitative Light Imaging Laboratory Beckman Institute for Advanced Science and Technology University of Illinois at Urbana-Champaign UrbanaIL61801 United States Department of Bioengineering University of Illinois at Urbana-Champaign 306 N. Wright Street UrbanaIL61801 United States Division of Nutritional Sciences University of Illinois Urbana-ChampaignIL61801 United States Neuroscience Program University of Illinois at Urbana-Champaign UrbanaIL61801 United States Laboratory of Integrative Immunology & Behavior Department of Animal Sciences University of Illinois Urbana-ChampaignIL61801 United States Department of Electrical and Computer Engineering University of Illinois at Urbana-Champaign 306 N. Wright Street UrbanaIL61801 United States Abbott Nutrition Discovery Research ColumbusOH43219 United States Abbott Nutrition Strategic Research 3300 Stelzer Road ColumbusOH United States

Deficient myelination of the brain is associated with neurodevelopmental delays, particularly in high-risk infants, such as those born small in relation to their gestational age (SGA). New methods are needed to further study this condition. Here, we employ Color Spatial Light Interference Microscopy (cSLIM), which uses a brightfield objective and RGB camera to generate pathlength-maps with nanoscale sensitivity in conjunction with a regular brightfield image. Using tissue sections stained with Luxol Fast Blue, the myelin structures were segmented from a brightfield image. Using a binary mask, those portions were quantitatively analyzed in the corresponding phase maps. We first used the CLARITY method to remove tissue lipids and validate the sensitivity of cSLIM to lipid content. We then applied cSLIM to brain histology slices. These specimens are from a previous MRI study, which demonstrated that appropriate for gestational age (AGA) piglets have increased internal capsule myelination (ICM) compared to small for gestational age (SGA) piglets and that a hydrolyzed fat diet improved ICM in both. The identity of samples was blinded until after statistical analyses. Copyright © 2020, The Authors. All rights reserved.

关键词： Brain

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Cytocompatible magnetostrictive microstructures for nano- A nd microparticle manipulation on linear strain response piezoelectrics

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Multifunctional Materials 2018年第1期1卷

作者： Xiao, Zhuyun Khojah, Reem Chooljian, Marc Conte, Roberto Lo Schneider, Joseph D. Fitzell, Kevin Chopdekar, Rajesh V. Wang, Yilian Scholl, Andreas Chang, Jane Carman, Gregory P. Bokor, Jeffrey Carlo, Dino Di Candler, Rob N. Department of Electrical and Computer Engineering University of California Los Angeles United States Department of Bioengineering University of California Los Angeles United States University of California Berkeley UCSF Graduate Program in Bioengineering Berkeley United States Department of Electrical Engineering and Computer Sciences University of California Berkeley United States Department of Mechanical and Aerospace Engineering University of California Los Angeles United States Department of Chemical and Biomolecular Engineering University of California Los Angeles United States Advanced Light Source Lawrence Berkeley National Laboratory Berkeley United States California Nanosystems Institute Los Angeles United States

In this work, we investigate polycrystalline Ni and FeGamagnetostrictivemicrostructures on prepoled (011)-cut single crystal [Pb(Mg1/3Nb2/3)O3]1-x-[PbTiO3]x (PMN-PT, x ≈ 0.31) with linear strain profile versus applied electric field. Magnetostrictive microstructure arrays with various geometries are patterned on PMN-PT. Functionalizedmagnetic beads are trapped by localized stray fields originating fromthe microstructures. With an applied electric field, themagnetic domains are actuated, inducing the motion of the coupled particles with sub-micrometer precision. This work shows promise of using energy-efficient electric-field-controlled magnetostrictivemicro- A nd nanostructures formanipulatingmagnetic beads via a linear strain response. The work also demonstrates the viability of cells suspended in solution on these structures when subject to applied electric fields, proving the cytocompatibility of the platform for live cell sorting applications. © 2018 IOP Publishing Ltd.

关键词： Electric fields

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Role of Integrating Diffusion Mr Image-Markers with Clinical-Biomarkers For Early Assessment of Renal Transplants

Role of Integrating Diffusion Mr Image-Markers with Clinical...

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IEEE International Conference on Image Processing

作者： Mohammad Shehata Mohammed Ghazal Garth Beache Mohamed Abou EI-Ghar Amy Dwyer Hassan Hajjdiab Ashraf Khalil Ayman EI-Baz Bioengineering Department University of Louisville Louisville KY USA Radiology Department University of Louisville Louisville KY USA Radiolozy Department Mansoura University Mansoura Egypt Kidney Disease Program University of Louisville Louisville KY USA Electrical and Computer Engineering Department Abu Dhabi University Abu Dhabi UAE Computer Science and Information Technology Department Abu Dhabi University Abu Dhabi UAE

Recently, diffusion-weighted magnetic resonance imaging (DW-MRI) has been explored for non-invasive assessment of renal transplant functions. In this paper, a computer-aided diagnostic (CAD) system is developed to assess renal transplant functionality, which integrates both clinical and diffusion MRI -derived markers extracted from 4D DW-MRI (i.e. 3D + b-value). To extract the DW-MR image-markers, our framework performs multiple image processing steps, including kidney segmentation using a level-set approach and estimation of image-markers. To extract these image-markers, apparent diffusion coefficients (ADCs) are estimated from the segmented DW-MRIs and cumulative distribution functions (CDFs) of the ADCs are constructed at different b-values (i.e. gradient field strengths and duration). Finally, these markers (i.e. CDFs) are integrated with clinical biomarkers (e.g., creatinine clearance and serum plasma creatinine) to assess transplant status using stacked auto-encoders with non-negativity constraints based on deep learning classification approach. Our CAD system consists of two consecutive classification stages. The first stage classifier achieved a 96% accuracy, a 95% sensitivity, and a 100% specificity in distinguishing non-rejection (NR) from dysfunctional (DF) transplanted kidneys. Additionally, an overall accuracy of 94% has been obtained in the second stage in separating DF to acute rejection (AR) and different renal disease (DRD) transplants. Our preliminary results hold strong promise that the presented CAD system is of a high reliability to non-invasively diagnose renal transplant status.

关键词： Kidney Diseases Three-dimensional displays Image segmentation Magnetic resonance imaging Machine learning Radio frequency

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StimDust: A 1.7 mm3, implantable wireless precision neural stimulator with ultrasonic power and communication

arXiv

引用

arXiv 2018年

作者： Piech, David K. Johnson, Benjamin C. Shen, Konlin Meraj Ghanbari, M. Li, Ka Yiu Neely, Ryan M. Kay, Joshua E. Carmena, Jose M. Maharbiz, Michel M. Muller, Rikky San Francisco Graduate Program in Bioengineering University of California Berkeley BerkeleyCA94720 United States Department of Electrical Engineering and Computer Sciences University of California Berkeley BerkeleyCA94720 United States Department of Electrical Engineering and Computer Engineering Boise State University BoiseID83725 United States Helen Wills Neuroscience Institute University of California Berkeley BerkeleyCA94720 United States Chan-Zuckerberg Biohub San FranciscoCA94158 United States

Neural stimulation is a powerful technique for modulating physiological functions and for writing information into the nervous system as part of brain-machine interfaces. Current clinically approved neural stimulators require batteries and are many cubic centimeters in size- typically much larger than their intended targets. We present a complete wireless neural stimulation system consisting of a 1.7 mm3 wireless, batteryless, leadless implantable stimulator (the "mote"), an ultrasonic wireless link for power and bi-directional communication, and a hand-held external transceiver. The mote consists of a piezoceramic transducer, an energy storage capacitor, and a stimulator integrated circuit (IC). The IC harvests ultrasonic power with high efficiency, decodes stimulation parameter downlink data, and generates current-controlled stimulation pulses. Stimulation parameters are time-encoded on the fly through the wireless link rather than being programmed and stored on the mote, enabling complex stimulation protocols with high-temporal resolution and closed-loop capability while reducing power consumption and on-chip memory requirements. Uplink data indicates whether the mote is currently stimulating;it is encoded by the mote via backscatter modulation and is demodulated at the external transceiver. We show that the system operates at an acoustic power one fifth the FDA limit for diagnostic ultrasound and is robust to expected real-world acoustic link misalignment. We investigate the performance of the system with motes acutely implanted with a cuff on the sciatic nerve of anesthetized rats and show highly repeatable stimulation across a wide range of physiological responses. Copyright © 2018, The Authors. All rights reserved.

关键词： Electrophysiology

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