Deep learning-based inpainting of saturation artifacts in optical coherence tomography images

Limited by the dynamic range of the detector,saturation artifacts usually occur in optical coherence tomography(OCT)imaging for high scattering media.The available methods are difficult to remove saturation artifacts and restore texture completely in OCT images.We proposed a deep learning-based inpainting method of saturation artifacts in this paper.The generation mechanism of saturation artifacts was analyzed,and experimental and simulated datasets were built based on the mechanism.Enhanced super-resolution generative adversarial networks were trained by the clear–saturated phantom image pairs.The perfect reconstructed results of experimental zebrafish and thyroid OCT images proved its feasibility,strong generalization,and robustness.

A promising approach for quantifying focal stroke modeling and assessing stroke progression:optical resolution photoacoustic microscopy photothrombosis

To investigate the mechanisms underlying the onset and progression of ischemic stroke,some methods have been proposed that can simultaneously monitor and create embolisms in the animal cerebral cortex.However,these methods often require complex systems and the effect of age on cerebral embolism has not been adequately studied,although ischemic stroke is strongly age-related.In this study,we propose an optical-resolution photoacoustic microscopy-based visualized photothrombosis methodology to create and monitor ischemic stroke in mice simultaneously using a 532 nm pulsed laser.We observed the molding process in mice of different ages and presented age-dependent vascular embolism differentiation.Moreover,we integrated optical coherence tomography angiography to investigate age-associated trends in cerebrovascular variability following a stroke.Our imaging data and quantitative analyses underscore the differential cerebrovascular responses to stroke in mice of different ages,thereby highlighting the technique's potential for evaluating cerebrovascular health and unraveling age-related mechanisms involved in ischemic strokes.

Intravascular photoacoustic and optical coherence tomography imaging dual-mode system for detecting spontaneous coronary artery dissection: A feasibility study

In this work,we present an intravascular dual-mode endoscopic system capable of both intravascular photoacoustic imaging(IVPAI)and intravascular optical coherence tomography(IVOCT)for recognizing spontaneous coronary artery dissection(SCAD)phantoms.IVPAI provides high-resolution and high-penetration images of intramural hematoma(IMH)at different depths,so it is especially useful for imaging deep blood clots associated with imaging phantoms.IVOCT can readily visualize the double-lumen morphology of blood vessel walls to identify intimal tears.We also demonstrate the capability of this dual-mode endoscopic system using mimicking phantoms and biological samples of blood clots in ex vivo porcine arteries.The results of the experiments indicate that the combined IVPAI and IVOCT technique has the potential to provide a more accurate SCAD assessment method for clinical applications.

Mangrove monitoring and extraction based on multi-source remote sensing data:a deep learning method based on SAR and optical image fusion

Mangroves are indispensable to coastlines,maintaining biodiversity,and mitigating climate change.Therefore,improving the accuracy of mangrove information identification is crucial for their ecological protection.Aiming at the limited morphological information of synthetic aperture radar(SAR)images,which is greatly interfered by noise,and the susceptibility of optical images to weather and lighting conditions,this paper proposes a pixel-level weighted fusion method for SAR and optical images.Image fusion enhanced the target features and made mangrove monitoring more comprehensive and accurate.To address the problem of high similarity between mangrove forests and other forests,this paper is based on the U-Net convolutional neural network,and an attention mechanism is added in the feature extraction stage to make the model pay more attention to the mangrove vegetation area in the image.In order to accelerate the convergence and normalize the input,batch normalization(BN)layer and Dropout layer are added after each convolutional layer.Since mangroves are a minority class in the image,an improved cross-entropy loss function is introduced in this paper to improve the model’s ability to recognize mangroves.The AttU-Net model for mangrove recognition in high similarity environments is thus constructed based on the fused images.Through comparison experiments,the overall accuracy of the improved U-Net model trained from the fused images to recognize the predicted regions is significantly improved.Based on the fused images,the recognition results of the AttU-Net model proposed in this paper are compared with its benchmark model,U-Net,and the Dense-Net,Res-Net,and Seg-Net methods.The AttU-Net model captured mangroves’complex structures and textural features in images more effectively.The average OA,F1-score,and Kappa coefficient in the four tested regions were 94.406%,90.006%,and 84.045%,which were significantly higher than several other methods.This method can provide some technical support for the monitoring and protection of mangrove ecosystems.

Reconstruction algorithm for cross-waveband optical computing imaging

In a single-pixel fast imaging setup,the data collected by the single-pixel detector needs to be processed by a computer,but the speed of the latter will affect the image reconstruction time.Here we propose two kinds of setups which are able to transform non-visible into visible light imaging,wherein their computing process is replaced by a camera integration mode.The image captured by the camera has a low contrast,so here we present an algorithm that can realize a high quality image in near-infrared to visible cross-waveband imaging.The scheme is verified both by simulation and in actual experiments.The setups demonstrate the great potential for single-pixel imaging and high-speed cross-waveband imaging for future practical applications.

Investigating the relationship between intracranial atherosclerotic plaque remodelling and diabetes using high-resolution vessel wall imaging

BACKGROUND Intracranial atherosclerosis,a leading cause of stroke,involves arterial plaque formation.This study explores the link between plaque remodelling patterns and diabetes using high-resolution vessel wall imaging(HR-VWI).AIM To investigate the factors of intracranial atherosclerotic remodelling patterns and the relationship between intracranial atherosclerotic remodelling and diabetes mellitus using HR-VWI.METHODS Ninety-four patients diagnosed with middle cerebral artery or basilar artery INTRODUCTION Intracranial atherosclerotic disease is one of the main causes of ischaemic stroke in the world,accounting for approx-imately 10%of transient ischaemic attacks and 30%-50%of ischaemic strokes[1].It is the most common factor among Asian people[2].The adaptive changes in the structure and function of blood vessels that can adapt to changes in the internal and external environment are called vascular remodelling,which is a common and important pathological mechanism in atherosclerotic diseases,and the remodelling mode of atherosclerotic plaques is closely related to the occurrence of stroke.Positive remodelling(PR)is an outwards compensatory remodelling where the arterial wall grows outwards in an attempt to maintain a constant lumen diameter.For a long time,it was believed that the degree of stenosis can accurately reflect the risk of ischaemic stroke[3-5].Previous studies have revealed that lesions without significant luminal stenosis can also lead to acute events[6,7],as summarized in a recent meta-analysis study in which approximately 50%of acute/subacute ischaemic events were due to this type of lesion[6].Research[8,9]has pointed out that the PR of plaques is more dangerous and more likely to cause acute ischaemic stroke.Previous studies[10-13]have found that there are specific vascular remodelling phenomena in the coronary and carotid arteries of diabetic patients.However,due to the deep location and small lumen of intracranial arteries and limitations of imaging techniques,the relationship between intracranial arterial remodelling and diabetes is still unclear.In recent years,with the development of magnetic resonance technology and the emergence of high-resolution(HR)vascular wall imaging,a clear and multidimensional display of the intracranial vascular wall has been achieved.Therefore,in this study,HR wall imaging(HR-VWI)was used to display the remodelling characteristics of bilateral middle cerebral arteries and basilar arteries and to explore the factors of intracranial vascular remodelling and its relationship with diabetes.

Experimental Study on the Variation of Optical Imaging Characteristics with Zenith Angle due to Internal Solitary Waves in Sunglint

Internal solitary waves(ISWs)change the roughness of the sea surface,thus producing dark and bright bands in optical images.However,reasons for changes in imaging characteristics with the solar zenith angle remain unclear.In this paper,the optical imaging pattern of ISWs in sunglint under different zenith angles of the light source is investigated by collecting optical images of ISWs through physical simulation.The experiment involves setting 10 zenith angles of the light source,which are divided into area a the optical images of ISWs in the three areas show dark-bright mode,single bright band,and bright-dark mode,which are consistent with those observed by optical remote sensing.In addition,this study analyzed the percentage of the dark and bright areas of the bands and the change in the relative gray difference and found changes in both areas under different zenith angles of the light source.The MODIS and ASAR images display a similar brightness-darkness distance of the same ISWs.Therefore,the relationship between the brightness-darkness distance and the characteristic half-width of ISWs is determined in accordance with the eKdV theory and the imaging mechanism of ISWs of the SAR image.Overall,the relationship between them in the experiment is almost consistent with the theoretical result.

MACS-W:A modified optical clearing agent for imaging 3D cell cultures

Three-dimensional(3D)cell cultures have contributed to a variety of biological research fields by filling the gap between monolayers and animal models.The modern optical sectioning microscopic methods make it possible to probe the complexity of 3D cell cultures but are limited by the inherent opaqueness.While tissue optical clearing methods have emerged as powerful tools for investigating whole-mount tissues in 3D,they often have limitations,such as being too harsh for fragile 3D cell cultures,requiring complex handling protocols,or inducing tissue deformation with shrinkage or expansion.To address this issue,we proposed a modified optical clearing method for 3D cell cultures,called MACS-W,which is simple,highly efficient,and morphology-preserving.In our evaluation of MACS-W,we found that it exhibits excellent clearing capability in just 10 min,with minimal deformation,and helps drug evaluation on tumor spheroids.In summary,MACS-W is a fast,minimally-deformative and fluorescence compatible clearing method that has the potential to be widely used in the studies of 3D cell cultures.

In vivo pilot study into superficial microcirculatory characteristics of colorectal adenomas using novel high-resolution magnifying endoscopy with blue laser imaging

BACKGROUND No studies have yet been conducted on changes in microcirculatory hemody-namics of colorectal adenomas in vivo under endoscopy.The microcirculation of the colorectal adenoma could be observed in vivo by a novel high-resolution magnification endoscopy with blue laser imaging(BLI),thus providing a new insight into the microcirculation of early colon tumors.AIM To observe the superficial microcirculation of colorectal adenomas using the novel magnifying colonoscope with BLI and quantitatively analyzed the changes in hemodynamic parameters.METHODS From October 2019 to January 2020,11 patients were screened for colon adenomas with the novel high-resolution magnification endoscope with BLI.Video images were recorded and processed with Adobe Premiere,Adobe Photoshop and Image-pro Plus software.Four microcirculation parameters:Microcirculation vessel density(MVD),mean vessel width(MVW)with width standard deviation(WSD),and blood flow velocity(BFV),were calculated for adenomas and the surrounding normal mucosa.RESULTS A total of 16 adenomas were identified.Compared with the normal surrounding mucosa,the superficial vessel density in the adenomas was decreased(MVD:0.95±0.18 vs 1.17±0.28μm/μm2,P<0.05).MVW(5.11±1.19 vs 4.16±0.76μm,P<0.05)and WSD(11.94±3.44 vs 9.04±3.74,P<0.05)were both increased.BFV slowed in the adenomas(709.74±213.28 vs 1256.51±383.31μm/s,P<0.05).CONCLUSION The novel high-resolution magnification endoscope with BLI can be used for in vivo study of adenoma superficial microcirculation.Superficial vessel density was decreased,more irregular,with slower blood flow.

High-resolution magnetic resonance imaging in the diagnosis and management of vertebral artery dissection:A case report

BACKGROUND Vertebral artery dissection(VAD)is a rare but life-threatening condition characterized by tearing of the intimal layer of the vertebral artery,leading to stenosis,occlusion or rupture.The clinical presentation of VAD can be heterogeneous,with common symptoms including headache,dizziness and balance problems.Timely diagnosis and treatment are crucial for favorable outcomes;however,VAD is often missed due to its variable clinical presentation and lack of robust diagnostic guidelines.High-resolution magnetic resonance imaging(HRMRI)has emerged as a reliable diagnostic tool for VAD,providing detailed visualization of vessel wall abnormalities.CASE SUMMARY A young male patient presented with an acute onset of severe headache,vomiting,and seizures,followed by altered consciousness.Imaging studies revealed bilateral VAD,basilar artery thrombosis,multiple brainstem and cerebellar infarcts,and subarachnoid hemorrhage.Digital subtraction angiography(DSA)revealed vertebral artery stenosis but failed to detect the dissection,potentially because intramural thrombosis obscured the VAD.In contrast,HRMRI confirmed the diagnosis by revealing specific signs of dissection.The patient was managed conservatively with antiplatelet therapy and other supportive measures,such as blood pressure control and pain management.After 5 mo of rehabilitation,the patient showed significant improvement in swallowing and limb strength.CONCLUSION HR-MRI can provide precise evidence for the identification of VAD.

The Application of Digital Optical 3D Image Analyzer Evaskin in The Evaluation of Wrinkles

To verify the effectiveness of digital optical 3D image analyzer EvaSKIN in the objective and quantitative evaluation of wrinkles.A total of 115 subjects were recruited,the facial images of the subjects were collected by digital optical 3D image analyzer and manual camera,the changes of crow’s feet with age were analyzed.Pictures obtained by manual photography can be directly used for observation and preliminary grading of wrinkles.However,the requirements for evaluators are high,and the results are prone to errors,which will affect the accuracy of the evaluation.Therefore,skilled raters are needed.Compared with the manual photography method,the digital optical 3D image analyzer EvaSKIN can realize three-dimensional extraction of wrinkles,and obtain the change trend of crow’s feet with age.20~30 years old,wrinkles begin to appear slowly;wrinkles will increase rapidly at the age of 30~50;The length of 50~60 year old wrinkles is basically fixed,the wrinkles develop longitudewise,gradually widen and deepen,and the area,depth and volume increase is obvious,and the skin aging condition is intensified.the digital optical 3D image analyzer EvaSKIN realizes the 3D extraction of wrinkles,quantifies the circumference,area,average depth,maximum depth and volume of wrinkles,realizes the objective and quantitative evaluation of wrinkle state,is more accurate in the measurement of wrinkles,and provides a new instrument and method for the evaluation of wrinkles.it is a perfect and supplement to the traditional evaluation methods,and to a certain extent,it helps the research and development and evaluation institutions of cosmetics to obtain more abundant and three-dimensional data support.

Segmentation of retinal fluid based on deep learning:application of three-dimensional fully convolutional neural networks in optical coherence tomography images

AIM: To explore a segmentation algorithm based on deep learning to achieve accurate diagnosis and treatment of patients with retinal fluid.METHODS: A two-dimensional(2D) fully convolutional network for retinal segmentation was employed. In order to solve the category imbalance in retinal optical coherence tomography(OCT) images, the network parameters and loss function based on the 2D fully convolutional network were modified. For this network, the correlations of corresponding positions among adjacent images in space are ignored. Thus, we proposed a three-dimensional(3D) fully convolutional network for segmentation in the retinal OCT images.RESULTS: The algorithm was evaluated according to segmentation accuracy, Kappa coefficient, and F1 score. For the 3D fully convolutional network proposed in this paper, the overall segmentation accuracy rate is 99.56%, Kappa coefficient is 98.47%, and F1 score of retinal fluid is 95.50%. CONCLUSION: The OCT image segmentation algorithm based on deep learning is primarily founded on the 2D convolutional network. The 3D network architecture proposed in this paper reduces the influence of category imbalance, realizes end-to-end segmentation of volume images, and achieves optimal segmentation results. The segmentation maps are practically the same as the manual annotations of doctors, and can provide doctors with more accurate diagnostic data.

Experimental investigation on the optical remote sensing images of internal solitary waves with a smooth surface

The parameter inversion of internal solitary waves (ISWs) based on optical remote sensing images is a key work. A new approach is proposed and demonstrated for simulating the optical remote sensing images of ISWs with a smooth surface in the laboratory. An optical remote sensing simulation system used to detect ISWs is constructed by a two-dimensional ISW flume, a LED (light emitting diode) light source and two CCD (charge coupled device) cameras. The optical remote sensing images of the horizontal surface and ISWs propagation images of a vertical side are detected simultaneously, which aims to explore the response of optical remote sensing corresponding to ISWs with the smooth surface. The results show that during the propagation of ISWs, dark pattern images are obtained by CCD 1 camera. The characteristics of the dark patterns vary along with the incident angle of the light source. The characteristic parameters of the optical remote sensing images correspond to the wave factors of vertical profiles. The experiment also shows a positive correlation between the dark pattern width and the half wave width under different amplitudes of ISWs. The system has the advantages of clear phenomenon and high repeatability, which provides the scientific basis for quantitative investigation on imaging mechanism of ISW by optical remote sensing.

Tree species classification using deep learning and RGB optical images obtained by an unmanned aerial vehicle

The diversity of tree species and the complexity of land use in cities create challenging issues for tree species classification.The combination of deep learning methods and RGB optical images obtained by unmanned aerial vehicles(UAVs) provides a new research direction for urban tree species classification.We proposed an RGB optical image dataset with 10 urban tree species,termed TCC10,which is a benchmark for tree canopy classification(TCC).TCC10 dataset contains two types of data:tree canopy images with simple backgrounds and those with complex backgrounds.The objective was to examine the possibility of using deep learning methods(AlexNet,VGG-16,and ResNet-50) for individual tree species classification.The results of convolutional neural networks(CNNs) were compared with those of K-nearest neighbor(KNN) and BP neural network.Our results demonstrated:(1) ResNet-50 achieved an overall accuracy(OA) of 92.6% and a kappa coefficient of 0.91 for tree species classification on TCC10 and outperformed AlexNet and VGG-16.(2) The classification accuracy of KNN and BP neural network was less than70%,while the accuracy of CNNs was relatively higher.(3)The classification accuracy of tree canopy images with complex backgrounds was lower than that for images with simple backgrounds.For the deciduous tree species in TCC10,the classification accuracy of ResNet-50 was higher in summer than that in autumn.Therefore,the deep learning is effective for urban tree species classification using RGB optical images.

Monitoring of vegetation coverage based on high-resolution images

Measurement of vegetation coverage on a small scale is the foundation for the monitoring of changes in vegetation coverage and of the inversion model of monitoring vegetation coverage on a large scale by remote sensing. Using the object-oriented analytical software, Definiens Professional 5, a new method for calculating vegetation coverage based on high-resolution images （aerial photographs or near-surface photography） is proposed. Our research supplies references to remote sensing measurements of vegetation coverage on a small scale and accurate fundamental data for the inversion model of vegetation coverage on a large and intermediate scale to improve the accuracy of remote sensing monitoring of changes in vegetation coverage.

Level study on fractal characteristics of tidal creeks and information of seashell habitats in the Gaizhou Beach based on high-resolution satellite images

The fractal characteristics of tidal creeks in the Gaizhou Beach are analyzed based on high-resolution images fusionof Landsat TM and ERS2, and then the graphic models and characteristics of converse information tree of tidalcreeks in the Gaizhou Beach are established. A calculation model is established based on the above results, and at thesame time, quantitative calculation of the evolution characteristics and the diversity between the northern and thesouthern parts of the Gaizhou Beach is carried out. By the supervised classification of these images, distribution andareas of high tidal flats, middle tidal flats and low tidal flats in the Gaizhou Beach are studied quantitatively, and imagecharactistics of seashell habitats in the Gaizhou Beach and the correlation between mudflat distribution and seashellhabitats are studied. At last, the engineering problems in the Gaizhou Beach are discussed.

Coherence-coefficient-based Markov random field approach for building segmentation from high-resolution SAR images

Building segmentation from high-resolution synthetic aperture radar (SAR) images has always been one of the important research issues. Due to the existence of speckle noise and multipath effect, the pixel values change drastically, causing the large intensity differences in pixels of building areas. Moreover, the geometric structure of buildings can cause strong scattering spots, which brings difficulties to the segmentation and extraction of buildings. To solve of these problems, this paper presents a coherence-coefficient-based Markov random field (CCMRF) approach for building segmentation from high-resolution SAR images. The method introduces the coherence coefficient of interferometric synthetic aperture radar (InSAR) into the neighborhood energy based on traditional Markov random field (MRF), which makes interferometric and spatial contextual information more fully used in SAR image segmentation. According to the Hammersley-Clifford theorem, the problem of maximum a posteriori (MAP) for image segmentation is transformed into the solution of minimizing the sum of likelihood energy and neighborhood energy. Finally, the iterative condition model (ICM) is used to find the optimal solution. The experimental results demonstrate that the proposed method can segment SAR building effectively and obtain more accurate results than the traditional MRF method and K-means clustering.

Cerebrovascular segmentation from mesoscopic optical images using Swin Transformer

Vascular segmentation is a crucial task in biomedical image processing,which is significant for analyzing and modeling vascular networks under physiological and pathological states.With advances in fluorescent labeling and mesoscopic optical techniques,it has become possible to map the whole-mouse-brain vascular networks at capillary resolution.However,segmenting vessels from mesoscopic optical images is a challenging task.The problems,such as vascular signal discontinuities,vessel lumens,and background fluorescence signals in mesoscopic optical images,belong to global semantic information during vascular segmentation.Traditional vascular segmentation methods based on convolutional neural networks(CNNs)have been limited by their insufficient receptive fields,making it challenging to capture global semantic information of vessels and resulting in inaccurate segmentation results.Here,we propose SegVesseler,a vascular segmentation method based on Swin Transformer.SegVesseler adopts 3D Swin Transformer blocks to extract global contextual information in 3D images.This approach is able to maintain the connectivity and topology of blood vessels during segmentation.We evaluated the performance of our method on mouse cerebrovascular datasets generated from three different labeling and imaging modalities.The experimental results demonstrate that the segmentation effect of our method is significantly better than traditional CNNs and achieves state-of-the-art performance.

Smart Photogrammetric and Remote Sensing Image Processing for Very High Resolution Optical Images——Examples from the CRC-AGIP Lab at UNB

This paper introduces some of the image processing techniques developed in the Canada Research Chair in Advanced Geomatics Image Processing Laboratory (CRC-AGIP Lab) and in the Department of Geodesy and Geomatics Engineering (GGE) at the University of New Brunswick (UNB), Canada. The techniques were developed by innovatively/“smartly” utilizing the characteristics of the available very high resolution optical remote sensing images to solve important problems or create new applications in photogrammetry and remote sensing. The techniques to be introduced are: automated image fusion (UNB-PanSharp), satellite image online mapping, street view technology, moving vehicle detection using single set satellite imagery, supervised image segmentation, image matching in smooth areas, and change detection using images from different viewing angles. Because of their broad application potential, some of the techniques have made a global impact, and some have demonstrated the potential for a global impact.

High-resolution Imaging of Living Retina through Optic Adaptive Retinal Imaging System

Purpose: To evaluate the possibility as well as the usage of adaptive optics in high-resolution retinal imaging.Methods:From March to November 2001, the fundus of 25 adults were checked by using Optic Adaptive Retinal Imaging System (OAS). The age of the subjects varied from 18～48 years. All had normal visual acuity from 0.9 to 1.0. No abnormality was found in the ocular examination, and their medical as well as ocular history was unremarkable. Results: High-resolution images of the retinal cells, photoreceptor and bipolar cell, were analysed. In these images, the cells are clearly resolved. The density of the photoreceptor at area 1.5 degree from the foveloa is around 40 000～50 000/mm2. At area 3 degree, it drops to less than 30 000/mm2.Conclusion:Optic Adaptive Retinal Imaging System (AOS) is able to get high-resolution image of retinal cells in living human eyes. It may be widely used in ophthalmology experimentally and clinically.