scholarly journals A novel optical needle probe for deep learning-based tissue elasticity characterization

2021 ◽  
Vol 7 (1) ◽  
pp. 21-25
Author(s):  
R. Mieling ◽  
J. Sprenger ◽  
S. Latus ◽  
L. Bargsten ◽  
A. Schlaefer
Keyword(s):  
Deep Learning ◽  
Tissue Characterization ◽  
Benign Tumors ◽  
Optical Coherence ◽  
Tissue Elasticity ◽  
Load Sensing ◽  
Needle Probe ◽  
Sample Characterization ◽  
Oct Imaging ◽  
Malignant And Benign Tumors

Abstract The distinction between malignant and benign tumors is essential to the treatment of cancer. The tissue's elasticity can be used as an indicator for the required tissue characterization. Optical coherence elastography (OCE) probes have been proposed for needle insertions but have so far lacked the necessary load sensing capabilities. We present a novel OCE needle probe that provides simultaneous optical coherence tomography (OCT) imaging and load sensing at the needle tip. We demonstrate the application of the needle probe in indentation experiments on gelatin phantoms with varying gelatin concentrations. We further implement two deep learning methods for the end-toend sample characterization from the acquired OCT data. We report the estimation of gelatin sample weight ratios [wt%] in unseen samples with a mean error of 1.21 ± 0.91 wt%. Both evaluated deep learning models successfully provide sample characterization with different advantages regarding the accuracy and inference time.

scholarly journals Synthetic polarization-sensitive optical coherence tomography by deep learning

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Yi Sun ◽  
Jianfeng Wang ◽  
Jindou Shi ◽  
Stephen A. Boppart
Keyword(s):  
Optical Coherence Tomography ◽  
Deep Learning ◽  
Imaging System ◽  
Imaging Modality ◽  
Similarity Index ◽  
Polarization Sensitivity ◽  
Imaging Method ◽  
Optical Coherence ◽  
The Real ◽  
Oct Imaging

AbstractPolarization-sensitive optical coherence tomography (PS-OCT) is a high-resolution label-free optical biomedical imaging modality that is sensitive to the microstructural architecture in tissue that gives rise to form birefringence, such as collagen or muscle fibers. To enable polarization sensitivity in an OCT system, however, requires additional hardware and complexity. We developed a deep-learning method to synthesize PS-OCT images by training a generative adversarial network (GAN) on OCT intensity and PS-OCT images. The synthesis accuracy was first evaluated by the structural similarity index (SSIM) between the synthetic and real PS-OCT images. Furthermore, the effectiveness of the computational PS-OCT images was validated by separately training two image classifiers using the real and synthetic PS-OCT images for cancer/normal classification. The similar classification results of the two trained classifiers demonstrate that the predicted PS-OCT images can be potentially used interchangeably in cancer diagnosis applications. In addition, we applied the trained GAN models on OCT images collected from a separate OCT imaging system, and the synthetic PS-OCT images correlate well with the real PS-OCT image collected from the same sample sites using the PS-OCT imaging system. This computational PS-OCT imaging method has the potential to reduce the cost, complexity, and need for hardware-based PS-OCT imaging systems.

scholarly journals Classification of Material Type from Optical Coherence Tomography Images Using Deep Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-6
Author(s):  
Metin Sabuncu ◽  
Hakan Ozdemir
Keyword(s):  
Optical Coherence Tomography ◽  
Deep Learning ◽  
Fiber Material ◽  
Fiber Types ◽  
Optical Coherence ◽  
Identification Procedure ◽  
Recycling Industry ◽  
Material Type ◽  
Oct Imaging

Classification of material type is crucial in the recycling industry since good quality recycling depends on the successful sorting of various materials. In textiles, the most commonly used fiber material types are wool, cotton, and polyester. When recycling fabrics, it is critical to identify and sort various fiber types quickly and correctly. The standard method of determining fabric fiber material type is the burn test followed by a microscopic examination. This traditional method is destructive, tedious, and slow since it involves cutting, burning, and examining the yarn of the fabric. We demonstrate that the identification procedure can be done nondestructively using optical coherence tomography (OCT) and deep learning. The OCT image scans of fabrics that are composed of different fiber material types such as wool, cotton, and polyester are used to train a deep neural network. We present the results of the created deep learning models’ capability to classify fabric fiber material types. We conclude that fiber material types can be identified nondestructively with high precision and recall by OCT imaging and deep learning. Because classification of material type can be performed by OCT and deep learning, this novel technique can be employed in recycling plants in sorting wool, cotton, and polyester fabrics automatically.

A new type of phytotherapeutic approach with angiosperms from arid zones of northern Mexico in patients with malignant and benign tumors

Planta Medica ◽  
2009 ◽  
Vol 75 (09) ◽  
Author(s):  
CA López-Moreno ◽  
LR Quintanilla ◽  
GLB Serrano ◽  
QE Rosales ◽  
FJA Pérez ◽  
...  
Keyword(s):  
Benign Tumors ◽  
Arid Zones ◽  
Northern Mexico ◽  
New Type ◽  
Malignant And Benign Tumors

Dynamics of biochemical and immunological parameters in patients with malignant and benign tumors of the thoracic and lumbar spine

2017 ◽  
Vol 0 (2) ◽  
pp. 30-34
Author(s):  
Mykola Korzh ◽  
Volodymyr Radchenko ◽  
Frieda Leontyeva ◽  
Volodymyr Kutsenko ◽  
Bogdan Shevtsov ◽  
...  
Keyword(s):  
Lumbar Spine ◽  
Benign Tumors ◽  
Immunological Parameters ◽  
Thoracic And Lumbar Spine ◽  
Malignant And Benign Tumors

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

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.

Blood Vessel Density Measured Using Dynamic Optical Coherence Tomography is a Tool for Wound Healers

2021 ◽  
pp. 153473462110173
Author(s):  
Rajgopal Mani ◽  
Jon Holmes ◽  
Kittipan Rerkasem ◽  
Nikolaos Papanas
Keyword(s):  
Blood Flow ◽  
Optical Coherence Tomography ◽  
Blood Vessel ◽  
Chronic Wounds ◽  
Vessel Density ◽  
Optical Coherence ◽  
Skin Microcirculation ◽  
Wound Edge ◽  
Venous Ulcers ◽  
Oct Imaging

Dynamic optical coherence tomography (D-OCT) is a relatively new technique that may be used to study the substructures in the retina, in the skin and its microcirculation. Furthermore, D-OCT is a validated method of imaging blood flow in skin microcirculation. The skin around venous and mixed arterio-venous ulcers was imaged and found to have tortuous vessels assumed to be angiogenic sprouts, and classified as dots, blobs, coils, clumps, lines, and curves. When these images were analyzed and measurements of vessel density were made, it was observed that the prevalence of coils and clumps in wound borders was significantly greater compared with those at wound centers. This reinforced the belief of inward growth of vessels from wound edge toward wound center which, in turn, reposed confidence in following the wound edge to study healing. D-OCT imaging permits the structure and the function of the microcirculation to be imaged, and vessel density measured. This offers a new vista of skin microcirculation and using it, to better understand angiogenesis in chronic wounds.

Feasibility and Clinical Utility of Ultra-Widefield–Navigated Swept-Source Optical Coherence Tomography Imaging

2021 ◽  
pp. 247412642199733
Author(s):  
Kyle D. Kovacs ◽  
M. Abdallah Mahrous ◽  
Luis Gonzalez ◽  
Benjamin E. Botsford ◽  
Tamara L. Lenis ◽  
...  
Keyword(s):  
Optical Coherence Tomography ◽  
Clinical Utility ◽  
Clinical Decision Making ◽  
Imaging System ◽  
Imaging Modality ◽  
Case Series ◽  
Subretinal Fluid ◽  
Optical Coherence ◽  
Swept Source ◽  
Oct Imaging

Purpose: This work aims to evaluate the clinical utility and feasibility of a novel scanning laser ophthalmoscope-based navigated ultra-widefield swept-source optical coherence tomography (UWF SS-OCT) imaging system. Methods: A retrospective, single-center, consecutive case series evaluated patients between September 2019 and October 2020 with UWF SS-OCT (modified Optos P200TxE, Optos PLC) as part of routine retinal care. The logistics of image acquisition, interpretability of images captured, nature of the peripheral abnormality, and clinical utility in management decisions were recorded. Results: Eighty-two eyes from 72 patients were included. Patients were aged 59.4 ± 17.1 years (range, 8-87 years). During imaging, 4.4 series of images were obtained in 4.1 minutes, with 86.4% of the image series deemed to be diagnostic of the peripheral pathology on blinded image review. The most common pathologic findings were chorioretinal scars (18 eyes). In 31 (38%) eyes, these images were meaningful in supporting clinical decision-making with definitive findings. Diagnoses imaged included retinal detachment combined with retinoschisis, retinal hole with overlying vitreous traction and subretinal fluid, vitreous inflammation overlying a peripheral scar, Coats disease, and peripheral retinal traction in sickle cell retinopathy. Conclusions: Navigated UWF SS-OCT imaging was clinically practical and provided high-quality characterization of peripheral retinal lesions for all eyes. Images directly contributed to management plans, including laser, injection or surgical treatment, for a clinically meaningful set of patients (38%). Future studies are needed to further assess the value of this imaging modality and its role in diagnosing, monitoring, and treating peripheral lesions.

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

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.

Sign in / Sign up

Export Citation Format

Share Document