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Assessing microscope image focus quality with deep learning

Samuel J. Yang, Marc Berndl, D. Michael Ando, Mariya Barch, Arunachalam Narayanaswamy, Eric Christiansen, Stephan Hoyer, Chris Roat, Jane Hung, Curtis T. Rueden, Asim Shankar, Steven Finkbeiner (+1 others)
2018 BMC Bioinformatics  
Conclusions: Our deep neural network enables classification of out-of-focus microscope images with both higher accuracy and greater precision than previous approaches via interpretable patch-level focus  ...  Large image datasets acquired on automated microscopes typically have some fraction of low quality, out-of-focus images, despite the use of hardware autofocus systems.  ...  Acknowledgments We thank Lusann Yang for reviewing the software, Claire McQuin and Allen Goodman for assistance with CellProfiler integration, Michael Frumkin for supporting the project and Anne Carpenter  ... 
doi:10.1186/s12859-018-2087-4 pmid:29540156 pmcid:PMC5853029 fatcat:bts2736mo5dtva3alwm2wmm7na

Deep-Learning Based Autofocus Score Prediction of Scanning Electron Microscope

Huisoo Kim, Moohyun Oh, Heerang Lee, Jonggyu Jang, Myeung Un Kim, Hyun Jong Yang, Michael Ryoo, Junhee Lee
2019 Microscopy and Microanalysis  
Specifically, we develop a deep learning computer software that uses an input of a sample image and current control parameters such as brightness, contrast, and focus to automatically score the quality  ...  As a result, the total number of images is 2134. For supervised learning, the deep learning network should be trained with known inputs and outputs.  ...  Specifically, we develop a deep learning computer software that uses an input of a sample image and current control parameters such as brightness, contrast, and focus to automatically score the quality  ... 
doi:10.1017/s1431927619001648 fatcat:yv7bpc4e7rc57gfintspqhvwwy

Deep learning Framework for Mobile Microscopy [article]

Anatasiia Kornilova, Mikhail Salnikov, Olga Novitskaya, Maria Begicheva, Egor Sevriugov, Kirill Shcherbakov, Valeriya Pronina, Dmitry V. Dylov
2021 arXiv   pre-print
Mobile microscopy is a promising technology to assist and to accelerate disease diagnostics, with its widespread adoption being hindered by the mediocre quality of acquired images.  ...  DeblurGAN architecture for image deblurring, (3) FuseGAN model for combining in-focus parts from multiple images to boost the detail.  ...  ACKNOWLEDGMENTS We would like to express our gratitude to the company MEL Science that provided us with mobile microscopy dataset. No funding was received for conducting this study.  ... 
arXiv:2007.13701v3 fatcat:wm4l36ecujhtzohwnpaphvbhoi

Robust Deep-Learning Based Autofocus Score Prediction for Scanning Electron Microscope

Hyun Jong Yang, Moohyun Oh, Jonggyu Jang, Hyeonsu Lyu, Junhee Lee
2020 Microscopy and Microanalysis  
Deep-learning has come into its own in analyzing scanning electron microscope (SEM) images [1] .  ...  As the image analysis part is becoming autonomous with the aid of deep-learning, the main challenge in the use of SEMs is to get high quality images by deftly controlling SEM parameters such as brightness  ...  Deep Neural Network Design: In our new DNN, magnification is also treated as an important input along with SEM images.  ... 
doi:10.1017/s1431927620015573 fatcat:3guief4ox5awjahqipkvbtg574

Deep learning extended depth-of-field microscope for fast and slide-free histology

Lingbo Jin, Yubo Tang, Yicheng Wu, Jackson B Coole, Melody T Tan, Xuan Zhao, Hawraa Badaoui, Jacob T Robinson, Michelle D Williams, Ann M Gillenwater, Rebecca R Richards-Kortum, Ashok Veeraraghavan
2020 Proceedings of the National Academy of Sciences of the United States of America  
Here, we introduce a deep-learning extended DOF (DeepDOF) microscope to quickly image large areas of freshly resected tissue to provide histologic-quality images of surgical margins without physical sectioning  ...  With the capability to quickly scan intact samples with subcellular detail, the DeepDOF microscope can improve tissue sampling during intraoperative tumor-margin assessment, while offering an affordable  ...  Here, we introduce a deep-learning extended DOF (DeepDOF) microscope to quickly image large areas of freshly resected tissue to provide histologic-quality images of surgical margins without physical sectioning  ... 
doi:10.1073/pnas.2013571117 pmid:33318169 pmcid:PMC7776814 fatcat:lohomahpdnaszmawpsog5kjgbi

It's easy to fool yourself: Case studies on identifying bias and confounding in bio-medical datasets [article]

Subhashini Venugopalan, Arunachalam Narayanaswamy, Samuel Yang, Anton Geraschenko, Scott Lipnick, Nina Makhortova, James Hawrot, Christine Marques, Joao Pereira, Michael Brenner, Lee Rubin, Brian Wainger (+1 others)
2020 arXiv   pre-print
They present an even greater challenge when we combine them with black-box machine learning techniques that operate on raw data. This work presents two case studies.  ...  These are cautionary tales on the limits of using machine learning techniques on raw data from scientific experiments.  ...  We then used these deep neural nets to understand the focus quality differences among various wells.  ... 
arXiv:1912.07661v2 fatcat:zpcaka6jkve4jeyq3bbtl6igkm

Microscope 2.0: An Augmented Reality Microscope with Real-time Artificial Intelligence Integration [article]

Po-Hsuan Cameron Chen, Krishna Gadepalli, Robert MacDonald, Yun Liu, Kunal Nagpal, Timo Kohlberger, Jeffrey Dean, Greg S. Corrado, Jason D. Hipp, Martin C. Stumpe
2018 arXiv   pre-print
One key clinical application has been in cancer histopathology, where the microscopic assessment of the tissue samples is used for the diagnosis and staging of cancer and thus guides clinical therapy.  ...  In this regard, Artificial Intelligence (AI) based tools promise to improve the access and quality of healthcare.  ...  Author contributions P.C. led the deep learning algorithm development and evaluation, K.G. led the software integration, R.M. led the optics development, Y.L. prepared data for the lymph node metastases  ... 
arXiv:1812.00825v2 fatcat:5rlqdkek6nfq7beivdoemnavpu

Fast Multi-Focus Fusion Based on Deep Learning for Early-Stage Embryo Image Enhancement

Vidas Raudonis, Agne Paulauskaite-Taraseviciene, Kristina Sutiene
2021 Sensors  
Methods: Multi-focus image fusion approach based on deep learning U-Net architecture is proposed in the paper, which allows reducing the amount of data up to 7 times without losing spectral information  ...  required for embryo enhancement in the microscopic image.  ...  Deep learning methods (specifically CNN based approaches) are often incorporated to solve blurring-effect problems through the ability to learn the focus measure to recognize the focused and defocused  ... 
doi:10.3390/s21030863 pmid:33525420 fatcat:6yvkkmotsfbc5mzxpw463piufi

Real-time multi-focus biomedical microscopic image fusion based on m-SegNet

Ronghao Pei, Weiwei Fu, Kang Yao, Tianli Zheng, Shangshang Ding, Hetong Zhang, Yang Zhang
2021 IEEE Photonics Journal  
Therefore, a new multi-focus microscopic image fusion method is proposed in this paper to quickly fuse multiple histological microscopic images from different focusing planes to generate full-focus images  ...  Index Terms: Histological microscopic image, Segnet network, multi-focus microscopic image fusion, parallel fusion strategy.  ...  Acknowledgment The authors would like to thank the anonymous reviewers and editor for their helpful and valuable comments, which substantially improved the quality of the paper.The authors sincerely thank  ... 
doi:10.1109/jphot.2021.3073022 fatcat:bqt4cmldn5d4lbar77gxrxsvmq

Fully Automated Cultivation of Adipose-Derived Stem Cells in the StemCellDiscovery—A Robotic Laboratory for Small-Scale, High-Throughput Cell Production Including Deep Learning-Based Confluence Estimation

Jelena Ochs, Ferdinand Biermann, Tobias Piotrowski, Frederik Erkens, Bastian Nießing, Laura Herbst, Niels König, Robert H. Schmitt
2021 Processes  
The StemCellDiscovery provides an in-line visual quality control for automated confluence estimation, which is realized by combining high-speed microscopy with deep learning-based image processing.  ...  While the system can handle different kinds of adherent cells, here, we focus on the cultivation of adipose-derived hMSCs.  ...  Each well was assessed through a microscope daily for six to seven days and processed with the deep learning-based image processing algorithm, giving a total of six confluence curves for each of the six-well  ... 
doi:10.3390/pr9040575 fatcat:fenboi2unrd7hmolej4qnggkum

Attention-guided Quality Assessment for Automated Cryo-EM Grid Screening [article]

Hong Xu, David E. Timm, Shireen Y. Elhabian
2020 arXiv   pre-print
Here, we focus on automating the early decision making for the microscope operator, scoring low magnification images of squares, and proposing the first deep learning framework, XCryoNet, for automated  ...  XCryoNet is a semi-supervised, attention-guided deep learning approach that provides explainable scoring of automatically extracted square images using limited amounts of labeled data.  ...  deep network to provide scores representing the quality of said squares.  ... 
arXiv:2007.05593v2 fatcat:geq34cu4pzginpyjfgdakjb4yy

SlideNet: Fast and Accurate Slide Quality Assessment Based on Deep Neural Networks [article]

Teng Zhang, Johanna Carvajal, Daniel F. Smith, Kun Zhao, Arnold Wiliem, Peter Hobson, Anthony Jennings, Brian C. Lovell
2018 arXiv   pre-print
In order to address the quality assessment problem, we propose a deep neural network based framework to automatically assess the slide quality in a semantic way.  ...  If the system detects severe damage in the slides, it could notify the experts that manual microscope reading may be required.  ...  One publicly available dataset with Gram stain images focuses on the classification of micro-organisms within the image slides [10] .  ... 
arXiv:1803.07240v1 fatcat:fdncaxidcbehtfe4ipqtnvbdu4

Point-of-care mobile digital microscopy and deep learning for the detection of soil-transmitted helminths and Schistosoma haematobium

Oscar Holmström, Nina Linder, Billy Ngasala, Andreas Mårtensson, Ewert Linder, Mikael Lundin, Hannu Moilanen, Antti Suutala, Vinod Diwan, Johan Lundin
2017 Global Health Action  
Parasites in the images were identified by visual examination and by analysis with a deep learningbased image analysis algorithm in the stool samples.  ...  Furthermore, we show that deep learning-based image analysis can be utilized for the automated detection and classification of helminths in the captured images. ARTICLE HISTORY  ...  Acknowledgments We would like to thank Taru Meri for assisting with the acquisition and preparation of the samples.  ... 
doi:10.1080/16549716.2017.1337325 pmid:28838305 pmcid:PMC5645671 fatcat:xt3lgb65bbcrhctmikohdw6b6u


D.A Landau
2021 Hematological Oncology  
Advantages and drawbacks of these techniques will be discussed with a special emphasis on the risk of biased performance assessment of deep learning systems.  ...  Furthermore, the calibrated qualities and the number of discrete pixels in a digital slide allow automated image analysis and quantification by using computer vision and, in particular, deep learning approaches  ... 
doi:10.1002/hon.9_2879 fatcat:jpc6yguirnb6rnctncjkucwtvm

Editorial: Computational Pathology

Behzad Bozorgtabar, Dwarikanath Mahapatra, Inti Zlobec, Tilman T. Rau, Jean-Philippe Thiran
2020 Frontiers in Medicine  
Emerging technologies such as whole slide imaging (WSI), have increasingly been used to improve the assessment of histological features with several advantages such as easy image accessibility and storage  ...  large-size and high-resolution whole-slide images; (2) Poor-quality images can decrease the decision accuracy of the ML pipelines.  ... 
doi:10.3389/fmed.2020.00245 pmid:32582734 pmcid:PMC7295903 fatcat:rmlcm3vv2rap3goc2vfedm6sem
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