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Repeatability and reproducibility of retinal nerve fibre layer thickness measurements with the iVue-100 optical coherence tomographer
摘要： Background: Accurate and repeatable measurements of the retinal nerve fibre layer (RNFL) thickness are important in the diagnosis and management of glaucoma and other disorders. Objective: To assess the repeatability and reproducibility of the iVue-100 optical coherence tomographer (OCT). Methods: The thickness of the RNFL was measured for 50 healthy participants using the iVue-100 OCT. Although both eyes per participant were measured, only right eyes were analysed here. Repeatability and reproducibility of the iVue-100 OCT were assessed using the intraclass correlation coefficient (ICC), coefficient of variation (CoV), paired t-tests and Bland-Altman analysis. Results: Good intra-observer repeatability was obtained as indicated by the ICC of observer 1 (range: 0.941 - 0.976) and observer 2 (range: 0.829 – 0.953) as well by the CoV of observer 1 (range: 0.098 – 0.137) and observer 2 (0.091 – 0.132). In terms of inter-observer reproducibility, significant differences (p< 0.05) in mean measurements between the observers were noted for the average RNFL readings and in the superior and inferior quadrants as assessed with paired t-tests. Even though significant inter-session differences were found for the average RNFL thickness and the superior quadrant (p = 0.003 and p = 0.013, respectively), excellent ICCs were obtained for inter-session reproducibility (range: 0.914 – 0.979). Conclusion: The iVue-100 OCT demonstrated good repeatability and reproducibility for RNFL thickness measurements.
关键词： Retinal nerve fibre layer thickness,optical coherence tomography,repeatability,iVue-100 OCT,reproducibility
更新于2025-09-23 15:22:29
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[IEEE 2018 IEEE International Symposium on Medical Measurements and Applications (MeMeA) - Rome, Italy (2018.6.11-2018.6.13)] 2018 IEEE International Symposium on Medical Measurements and Applications (MeMeA) - Extracting Features from Optical Coherence Tomography for Measuring Optical Nerve Thickness
摘要： Neurological pathologies, especially optical neuropathologies, can be studied by means of OCT (optical coherence tomography). Tomography generally allows to investigate inner structures of a tissue such as mass, and profiles of liquid flow. OCT is intended as an interferometry-based imaging technique that provides cross-sectional views of substrates. It allows to measure micro-scale cross-sectional imaging of biological tissue. While ultrasound uses sound waves, it acts like it but with a low coherence light. Optical nerve thickness has an impact on different neurological pathologies, and in particular as an indicator of epilepsy. We propose a dedicated technique for measuring optical nerve thickness and identifying its quality by means of processing front eye image in nanoscale. Experimental measurements have been performed, and a database of 10 teenagers has been used for that.
关键词： Micro and Nanotechnology,Optical nerve thickness measurement,Optical coherence Tomography,Neuro-disorders,Epilepsy,Atomic Force Microscopy,EEG
更新于2025-09-23 15:22:29
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Fully automated detection of retinal disorders by image-based deep learning
摘要： Purpose With the aging population and the global diabetes epidemic, the prevalence of age-related macular degeneration (AMD) and diabetic macular edema (DME) diseases which are the leading causes of blindness is further increasing. Intravitreal injections with anti-vascular endothelial growth factor (anti-VEGF) medications are the standard of care for their indications. Optical coherence tomography (OCT), as a noninvasive imaging modality, plays a major part in guiding the administration of anti-VEGF therapy by providing detailed cross-sectional scans of the retina pathology. Fully automating OCT image detection can significantly decrease the tedious clinician labor and obtain a faithful pre-diagnosis from the analysis of the structural elements of the retina. Thereby, we explore the use of deep transfer learning method based on the visual geometry group 16 (VGG-16) network for classifying AMD and DME in OCT images accurately and automatically. Method A total of 207,130 retinal OCT images between 2013 and 2017 were selected from retrospective cohorts of 5319 adult patients from the Shiley Eye Institute of the University of California San Diego, the California Retinal Research Foundation, Medical Center Ophthalmology Associates, the Shanghai First People’s Hospital, and the Beijing Tongren Eye Center, with 109,312 images (37,456 with choroidal neovascularization, 11,599 with diabetic macular edema, 8867 with drusen, and 51,390 normal) for the experiment. After images preprocessing, 1000 images (250 images from each category) from 633 patients were selected as validation dataset while the rest images from another 4686 patients were used as training dataset. We used deep transfer learning method to fine-tune the VGG-16 network pre-trained on the ImageNet dataset, and evaluated its performance on the validation dataset. Then, prediction accuracy, sensitivity, specificity, and receiver-operating characteristic (ROC) were calculated. Results Experimental results proved that the proposed approach had manifested superior performance in retinal OCT images detection, which achieved a prediction accuracy of 98.6%, with a sensitivity of 97.8%, a specificity of 99.4%, and introduced an area under the ROC curve of 100%. Conclusion Deep transfer learning method based on the VGG-16 network shows significant effectiveness on classification of retinal OCT images with a relatively small dataset, which can provide assistant support for medical decision-making. Moreover, the performance of the proposed approach is comparable to that of human experts with significant clinical experience. Thereby, it will find promising applications in an automatic diagnosis and classification of common retinal diseases.
关键词： Diabetic macular edema,Visual geometry group 16 network,Age-related macular degeneration,Optical coherence tomography,Deep transfer learning
更新于2025-09-23 15:22:29
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[IEEE 2018 3rd International Conference on Pattern Analysis and Intelligent Systems (PAIS) - Tebessa, Algeria (2018.10.24-2018.10.25)] 2018 3rd International Conference on Pattern Analysis and Intelligent Systems (PAIS) - A system for the automatic detection of glaucoma using retinal images
摘要： Glaucoma is an optic neuropathy and it principal cause of blindness in the world. In this paper, a system able to treat and analyze the Visual Field (VF) images and Optical Coherence Tomography of the Ganglion Cell Layer (OCT-GCL) images is proposed, in order to help early detection of glaucoma in its early stages. The proposed approach is based on calculating the percentage of healthy, sick and dead regions of VF and OCT-GCL images. In order to carry out this calculation, we combined the thresholding methods with morphological operators and median filter to extract all regions. These algorithms developed were tested on a set of images of a local database composed of 58 OCT-GCL images and 21 VF images. The results obtained are satisfactory and confirmed by experts in ophthalmology.
关键词： Optical coherence tomography of ganglion cell layer,Segmentation,Visual field,Glaucoma,Characterization
更新于2025-09-23 15:22:29
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Porosity Determination of Carbon and Glass Fibre Reinforced Polymers Using Phase-Contrast Imaging
摘要： This paper presents multi-modal image data of different fibre reinforced polymer samples acquired with a desktop Talbot-Lau grating interferometer (TLGI) X-ray computed tomography (XCT) system and compare the results with images acquired using conventional absorption-based XCT. Two different fibre reinforced polymer samples are investigated: (i) a carbon fibre reinforced polymer (CFRP) featuring a copper mesh embedded near the surface for lightning conduction and (ii) a short glass fibre reinforced polymer (GFRP) sample. The primary goal is the non-destructive detection of internal defects such as pores and the quantification of porosity. TLGI provides three imaging modalities including attenuation contrast (AC) due to absorption, differential phase contrast (DPC) due to refraction and dark-field contrast (DFC) due to scattering. In the case of the CFRP sample, DPC is less prone to metal streak artefacts improving the detection of pores that are located close to metal components. In addition, results of a metal artefact reduction (MAR) method, based on sinogram inpainting and an image fusion concept for AC, DPC and DPC, are presented. In the case of the GFRP sample, DPC between glass fibres and matrix is lower compared to AC while DPC shows an increased contrast between pores and its matrix. Porosity for the CFRP sample is determined by applying an appropriate global thresholding technique while an additional background removal is necessary for the GFRP sample.
关键词： Porosity,Carbon and glass fibre reinforced polymers,Talbot-Lau grating interferometer,X-ray computed tomography,Differential phase contrast
更新于2025-09-23 15:22:29
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[IEEE 2018 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI) - Guangzhou, China (2018.10.8-2018.10.12)] 2018 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI) - Automated Segmentation of Esophagus Layers from OCT Images Using Fast Marching Method
摘要： Thickness of the esophagus is an important diagnostic marker for many esophagus diseases. While labeling boundaries by manual to compute each layer’s average thickness is time-consuming and subjective. In this paper, we present a new fully automatic algorithm which includes Fast Marching Method (FMM) and Fourth-Order Runge-Kutta method (RK4) to identify five esophagus layers on optical coherence tomography (OCT) images. FMM is used to calculate the weighted geodesic distance. In particular, the velocity function involved in this method combines vertical gradient, horizontal gradient and curvature so that it not only can divide flat borders but also irregular borders. RK4 is used to find the shortest path which is the boundary to be segmented. The experimental comparison between automatic and manual is performed on 400 healthy guinea pig esophagus OCT images and the mean absolute error thickness difference between them is less than 6 pixels while the value can reach to 9.41 pixels at most between two observers.
关键词： Runge-Kutta method,Optical Coherence Tomography,Fast Marching Method,Image processing,Esophagus Layer Segmentation
更新于2025-09-23 15:22:29
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A unified framework for plasma data modelling in dynamic Positron Emission Tomography studies
摘要： Objective: Full quantification of dynamic PET data requires the knowledge of tracer concentration in the arterial plasma. However, its accurate measurement is challenging due to the presence of radiolabeled metabolites and measurement noise. Mathematical models are fitted to the plasma data for both radiometabolite correction and data denoising. However, the models used are generally not physiologically informed and not consistently applied across studies even when quantifying the kinetics of the same radiotracer, introducing methodological variability affecting the results interpretation. The aim of this study was to develop and validate a unified framework for the arterial data modelling to achieve an accurate and fully-automated description of the plasma tracer kinetics. Methods: The proposed pipeline employs basis pursuit techniques for estimating both radiometabolites and parent concentration models from the raw plasma measurements, allowing the resulting algorithm to be both robust and flexible to the different quality of data available. The pipeline was tested on four PET tracers ([11C]PBR28, [11C]MePPEP, [11C]WAY-100635 and [11C]PIB) with continuous and discrete blood sampling. Results: Compared to the standard procedure, the pipeline provided similar fit of the parent fraction but yielded a better description of the total plasma radioactivity, which in turn allowed a more accurate fit of the tissue PET data. Conclusion: The new method showed superior fits compared to the standard pipeline, for both continuous and discrete arterial sampling protocol, yielding to better description of PET data. Significance: The proposed pipeline has the potential to standardize the blood data modeling in dynamic PET studies given its robustness, flexibility and easiness of use.
关键词： Kinetic modelling,Positron Emission Tomography,Input function,Receptor imaging
更新于2025-09-23 15:22:29
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A thermo-responsive alginate nanogel platform co-loaded with gold nanoparticles and cisplatin for combined cancer chemo-photothermal therapy
摘要： The current interest in cancer research is being shifted from individual therapy to combinatorial therapy. In this contribution, a novel multifunctional nanoplatform comprising alginate nanogel co-loaded with cisplatin and gold nanoparticles (AuNPs) has been firstly developed to combine photothermal therapy and chemotherapy. The antitumor efficacy of the as-prepared nanocomplex was tested against CT26 colorectal tumor model. The nanocomplex showed an improved chemotherapy efficacy than free cisplatin and caused a significantly higher tumor inhibition rate. The in vivo thermometry results indicated that the tumors treated with the nanocomplex had faster temperature rise rate under 532 nm laser irradiation and received dramatically higher thermal doses due to optical absorption properties of AuNPs. The combined action of chemo-photothermal therapy using the nanocomplex dramatically suppressed tumor growth up to 95% of control and markedly prolonged the animal survival rate. Moreover, tumor metabolism was quantified by [18F]FDG (2-deoxy-2-[18F]fluoro-D-glucose)-positron emission tomography (PET) imaging and revealed that the combination of the nanocomplex and laser irradiation have the potential to eradicate microscopic residual tumor to prevent cancer relapse. Therefore, the nanocomplex can afford a potent anticancer efficacy whereby heat and drug can be effectively deliver to the tumor, and at the same time the high dose-associated side effects due to the separate application of chemotherapy and thermal therapy could be potentially reduced.
关键词： Alginate,Cisplatin,Gold nanoparticles,Chemo-photothermal therapy,Positron emission tomography
更新于2025-09-23 15:22:29
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[IEEE 2018 5th International Conference on Information Technology, Computer, and Electrical Engineering (ICITACEE) - Semarang (2018.9.27-2018.9.28)] 2018 5th International Conference on Information Technology, Computer, and Electrical Engineering (ICITACEE) - Compressive Sensing Approach with Double Layer Soft Threshold for ECVT Static Imaging
摘要： Electrical Capacitance Volume Tomography (ECVT) is a capacitance based tomography technology which is developed since its advantages on non-invasive properties, low energy, and portability. One of the challenge on developing this tomography technology is on its imaging algorithm. Naturally the imaging method forms under-determined linear system which is indicated by dimension of the measurement is much smaller compared to the projected value dimension. Mathematically it implies ill-posed inverse problem. Therefore Compressive Sensing framework is used to solve the corresponding inverse problem. To improve the accuracy of the predicted image reconstruction, new threshold approach, Double Layer Soft Threshold, is proposed and attached to the proposed Compressive Sensing based ECVT imaging method. The simulations results show that the proposed method is able to improve the conventional ECVT imaging method, Iterative Linear Back Projection (ILBP), by significantly eliminating the elongation error.
关键词： Imaging method,Electrical Capacitance Volume Tomography,Compressive Sensing,Double Layer Soft Threshold
更新于2025-09-23 15:22:29
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Feature Design for Classification from Tomosar Data
摘要： While previous work primarily focused on using Tomographic Synthetic Aperture Radar (TomoSAR) data to analyze the 3D structure of the imaged scene, we study its potential for the generation of semantic land cover maps in a supervised framework. We extract different features from the covariance matrices of a tomographic image stack as well as from the tomograms computed by tomographic focusing. To assess the impact of our approach, we compare our results to classification maps obtained from a fully polarimetric image. We show that it is possible to outperform classification results from polarimetric data by carefully designing hand-crafted features which can be extracted either from multi-baseline single polarization covariance matrices or from tomograms obtained after tomographic focusing. Our experiments show a significant gain in the classification accuracy, especially on challenging classes such as heterogeneous city and road.
关键词： machine learning,Synthetic Aperture Radar,feature extraction,tomography
更新于2025-09-23 15:22:29