Deep Learning for High Speed Optical Coherence Elastography

A high-speed, high-resolution swept-source optical coherence tomography (SS-OCT) is presented for focusing lens imaging and a k-domain uniform algorithm is adopted to find the wave number phase equalization. The radius of curvature of the laser focusing lens was obtained using a curve-fitting algorithm. The experimental results demonstrate that the measuring accuracy of the proposed SS-OCT system is higher than the laser confocal microscope. The SS-OCT system has great potential for surface topography measurement and defect inspection of the focusing lens.

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Development and validation of a deep learning system to screen vision-threatening conditions in high myopia using optical coherence tomography images

British Journal of Ophthalmology ◽

10.1136/bjophthalmol-2020-317825 ◽

2020 ◽

pp. bjophthalmol-2020-317825

Author(s):

Yonghao Li ◽

Weibo Feng ◽

Xiujuan Zhao ◽

Bingqian Liu ◽

Yan Zhang ◽

...

Keyword(s):

Optical Coherence Tomography ◽

Deep Learning ◽

High Myopia ◽

Large Scale ◽

Learning System ◽

Youden Index ◽

Optical Coherence ◽

Test Dataset ◽

Independent Test ◽

Independent Test Dataset

Background/aimsTo apply deep learning technology to develop an artificial intelligence (AI) system that can identify vision-threatening conditions in high myopia patients based on optical coherence tomography (OCT) macular images.MethodsIn this cross-sectional, prospective study, a total of 5505 qualified OCT macular images obtained from 1048 high myopia patients admitted to Zhongshan Ophthalmic Centre (ZOC) from 2012 to 2017 were selected for the development of the AI system. The independent test dataset included 412 images obtained from 91 high myopia patients recruited at ZOC from January 2019 to May 2019. We adopted the InceptionResnetV2 architecture to train four independent convolutional neural network (CNN) models to identify the following four vision-threatening conditions in high myopia: retinoschisis, macular hole, retinal detachment and pathological myopic choroidal neovascularisation. Focal Loss was used to address class imbalance, and optimal operating thresholds were determined according to the Youden Index.ResultsIn the independent test dataset, the areas under the receiver operating characteristic curves were high for all conditions (0.961 to 0.999). Our AI system achieved sensitivities equal to or even better than those of retina specialists as well as high specificities (greater than 90%). Moreover, our AI system provided a transparent and interpretable diagnosis with heatmaps.ConclusionsWe used OCT macular images for the development of CNN models to identify vision-threatening conditions in high myopia patients. Our models achieved reliable sensitivities and high specificities, comparable to those of retina specialists and may be applied for large-scale high myopia screening and patient follow-up.

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Power information network intrusion detection based on deep learning and cloud computing

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-219104 ◽

2021 ◽

pp. 1-9

Author(s):

Xiangbing Zhao ◽

Jianhui Zhou

Keyword(s):

Cloud Computing ◽

Deep Learning ◽

Intrusion Detection ◽

High Speed ◽

Information Networks ◽

Network Intrusion Detection ◽

Information Network ◽

Advantages And Disadvantages ◽

Network Intrusion ◽

Capture Mechanism

With the advent of the computer network era, people like to think in deeper ways and methods. In addition, the power information network is facing the problem of information leakage. The research of power information network intrusion detection is helpful to prevent the intrusion and attack of bad factors, ensure the safety of information, and protect state secrets and personal privacy. In this paper, through the NRIDS model and network data analysis method, based on deep learning and cloud computing, the demand analysis of the real-time intrusion detection system for the power information network is carried out. The advantages and disadvantages of this kind of message capture mechanism are compared, and then a high-speed article capture mechanism is designed based on the DPDK research. Since cloud computing and power information networks are the most commonly used tools and ways for us to obtain information in our daily lives, our lives will be difficult to carry out without cloud computing and power information networks, so we must do a good job to ensure the security of network information network intrusion detection and defense measures.

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Automatic Segmentation of Choroid Layer Using Deep Learning on Spectral Domain Optical Coherence Tomography

Applied Sciences ◽

10.3390/app11125488 ◽

2021 ◽

Vol 11 (12) ◽

pp. 5488

Author(s):

Wei Ping Hsia ◽

Siu Lun Tse ◽

Chia Jen Chang ◽

Yu Len Huang

Keyword(s):

Optical Coherence Tomography ◽

Deep Learning ◽

Choroidal Thickness ◽

Automatic Segmentation ◽

Average Error ◽

Good Prediction ◽

Optical Coherence ◽

Learning Method ◽

Subfoveal Choroidal Thickness ◽

Fully Connected

The purpose of this article is to evaluate the accuracy of the optical coherence tomography (OCT) measurement of choroidal thickness in healthy eyes using a deep-learning method with the Mask R-CNN model. Thirty EDI-OCT of thirty patients were enrolled. A mask region-based convolutional neural network (Mask R-CNN) model composed of deep residual network (ResNet) and feature pyramid networks (FPNs) with standard convolution and fully connected heads for mask and box prediction, respectively, was used to automatically depict the choroid layer. The average choroidal thickness and subfoveal choroidal thickness were measured. The results of this study showed that ResNet 50 layers deep (R50) model and ResNet 101 layers deep (R101). R101 U R50 (OR model) demonstrated the best accuracy with an average error of 4.85 pixels and 4.86 pixels, respectively. The R101 ∩ R50 (AND model) took the least time with an average execution time of 4.6 s. Mask-RCNN models showed a good prediction rate of choroidal layer with accuracy rates of 90% and 89.9% for average choroidal thickness and average subfoveal choroidal thickness, respectively. In conclusion, the deep-learning method using the Mask-RCNN model provides a faster and accurate measurement of choroidal thickness. Comparing with manual delineation, it provides better effectiveness, which is feasible for clinical application and larger scale of research on choroid.

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Foveal avascular zone segmentation in optical coherence tomography angiography images using a deep learning approach

Scientific Reports ◽

10.1038/s41598-020-80058-x ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Reza Mirshahi ◽

Pasha Anvari ◽

Hamid Riazi-Esfahani ◽

Mahsa Sardarinia ◽

Masood Naseripour ◽

...

Keyword(s):

Optical Coherence Tomography ◽

Diabetic Retinopathy ◽

Deep Learning ◽

Healthy Subjects ◽

Optical Coherence Tomography Angiography ◽

Validation Dataset ◽

Diabetic Patients ◽

Dice Similarity Coefficient ◽

Optical Coherence ◽

Avascular Zone

AbstractThe purpose of this study was to introduce a new deep learning (DL) model for segmentation of the fovea avascular zone (FAZ) in en face optical coherence tomography angiography (OCTA) and compare the results with those of the device’s built-in software and manual measurements in healthy subjects and diabetic patients. In this retrospective study, FAZ borders were delineated in the inner retinal slab of 3 × 3 enface OCTA images of 131 eyes of 88 diabetic patients and 32 eyes of 18 healthy subjects. To train a deep convolutional neural network (CNN) model, 126 enface OCTA images (104 eyes with diabetic retinopathy and 22 normal eyes) were used as training/validation dataset. Then, the accuracy of the model was evaluated using a dataset consisting of OCTA images of 10 normal eyes and 27 eyes with diabetic retinopathy. The CNN model was based on Detectron2, an open-source modular object detection library. In addition, automated FAZ measurements were conducted using the device’s built-in commercial software, and manual FAZ delineation was performed using ImageJ software. Bland–Altman analysis was used to show 95% limit of agreement (95% LoA) between different methods. The mean dice similarity coefficient of the DL model was 0.94 ± 0.04 in the testing dataset. There was excellent agreement between automated, DL model and manual measurements of FAZ in healthy subjects (95% LoA of − 0.005 to 0.026 mm2 between automated and manual measurement and 0.000 to 0.009 mm2 between DL and manual FAZ area). In diabetic eyes, the agreement between DL and manual measurements was excellent (95% LoA of − 0.063 to 0.095), however, there was a poor agreement between the automated and manual method (95% LoA of − 0.186 to 0.331). The presence of diabetic macular edema and intraretinal cysts at the fovea were associated with erroneous FAZ measurements by the device’s built-in software. In conclusion, the DL model showed an excellent accuracy in detection of FAZ border in enfaces OCTA images of both diabetic patients and healthy subjects. The DL and manual measurements outperformed the automated measurements of the built-in software.

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Intraoperative assessment of canine soft tissue sarcoma by deep learning enhanced optical coherence tomography

Veterinary and Comparative Oncology ◽

10.1111/vco.12747 ◽

2021 ◽

Author(s):

Yu Ye ◽

Weihong William Sun ◽

Ronald X. Xu ◽

Laura E. Selmic ◽

Mingzhai Sun

Keyword(s):

Optical Coherence Tomography ◽

Deep Learning ◽

Soft Tissue ◽

Soft Tissue Sarcoma ◽

Optical Coherence ◽

Intraoperative Assessment

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Characterization of Optical Coherence Tomography Images for Colon Lesion Differentiation under Deep Learning

Applied Sciences ◽

10.3390/app11073119 ◽

2021 ◽

Vol 11 (7) ◽

pp. 3119

Author(s):

Cristina L. Saratxaga ◽

Jorge Bote ◽

Juan F. Ortega-Morán ◽

Artzai Picón ◽

Elena Terradillos ◽

...

Keyword(s):

Optical Coherence Tomography ◽

Deep Learning ◽

Data Augmentation ◽

Optical Biopsy ◽

Colon Polyps ◽

Optical Coherence ◽

Colon Lesion ◽

Patient Prognosis ◽

B Scan Images

(1) Background: Clinicians demand new tools for early diagnosis and improved detection of colon lesions that are vital for patient prognosis. Optical coherence tomography (OCT) allows microscopical inspection of tissue and might serve as an optical biopsy method that could lead to in-situ diagnosis and treatment decisions; (2) Methods: A database of murine (rat) healthy, hyperplastic and neoplastic colonic samples with more than 94,000 images was acquired. A methodology that includes a data augmentation processing strategy and a deep learning model for automatic classification (benign vs. malignant) of OCT images is presented and validated over this dataset. Comparative evaluation is performed both over individual B-scan images and C-scan volumes; (3) Results: A model was trained and evaluated with the proposed methodology using six different data splits to present statistically significant results. Considering this, 0.9695 (±0.0141) sensitivity and 0.8094 (±0.1524) specificity were obtained when diagnosis was performed over B-scan images. On the other hand, 0.9821 (±0.0197) sensitivity and 0.7865 (±0.205) specificity were achieved when diagnosis was made considering all the images in the whole C-scan volume; (4) Conclusions: The proposed methodology based on deep learning showed great potential for the automatic characterization of colon polyps and future development of the optical biopsy paradigm.

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Two-axis magnetically-driven MEMS scanning catheter for endoscopic high-speed optical coherence tomography

Optics Express ◽

10.1364/oe.15.018130 ◽

2007 ◽

Vol 15 (26) ◽

pp. 18130 ◽

Cited By ~ 129

Author(s):

Ki Hean Kim ◽

B. Hyle Park ◽

Gopi N. Maguluri ◽

Tom W. Lee ◽

Fran J. Rogomentich ◽

...

Keyword(s):

Optical Coherence Tomography ◽

High Speed ◽

Optical Coherence

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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

Processes ◽

10.3390/pr9040575 ◽

2021 ◽

Vol 9 (4) ◽

pp. 575

Author(s):

Jelena Ochs ◽

Ferdinand Biermann ◽

Tobias Piotrowski ◽

Frederik Erkens ◽

Bastian Nießing ◽

...

Keyword(s):

Stem Cells ◽

Deep Learning ◽

High Throughput ◽

High Speed ◽

New Technologies ◽

Human Mesenchymal Stem Cells ◽

Simulation Software ◽

System Capacity ◽

Small Scale ◽

Production Environment

Laboratory automation is a key driver in biotechnology and an enabler for powerful new technologies and applications. In particular, in the field of personalized therapies, automation in research and production is a prerequisite for achieving cost efficiency and broad availability of tailored treatments. For this reason, we present the StemCellDiscovery, a fully automated robotic laboratory for the cultivation of human mesenchymal stem cells (hMSCs) in small scale and in parallel. While the system can handle different kinds of adherent cells, here, we focus on the cultivation of adipose-derived hMSCs. 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. We demonstrate the feasibility of the algorithm to detect hMSCs in culture at different densities and calculate confluences based on the resulting image. Furthermore, we show that the StemCellDiscovery is capable of expanding adipose-derived hMSCs in a fully automated manner using the confluence estimation algorithm. In order to estimate the system capacity under high-throughput conditions, we modeled the production environment in a simulation software. The simulations of the production process indicate that the robotic laboratory is capable of handling more than 95 cell culture plates per day.

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