Blood Vessels In Images Research Articles

Vascularity and Dynamic Contrast-Enhanced Breast Magnetic Resonance Imaging.

Angiogenesis is a key step in the initiation and progression of an invasive breast cancer. High microvessel density by morphological characterization predicts metastasis and poor survival in women with invasive breast cancers. However, morphologic characterization is subject to variability and only can evaluate a limited portion of an invasive breast cancer. Consequently, breast Magnetic Resonance Imaging (MRI) is currently being evaluated to assess vascularity. Recently, through the new field of radiomics, dynamic contrast enhanced (DCE)-MRI is being used to evaluate vascular density, vascular morphology, and detection of aggressive breast cancer biology. While DCE-MRI is a highly sensitive tool, there are specific features that limit computational evaluation of blood vessels. These include (1) DCE-MRI evaluates gadolinium contrast and does not directly evaluate biology, (2) the resolution of DCE-MRI is insufficient for imaging small blood vessels, and (3) DCE-MRI images are very difficult to co-register. Here we review computational approaches for detection and analysis of blood vessels in DCE-MRI images and present some of the strategies we have developed for co-registry of DCE-MRI images and early detection of vascularization.

Brain stroke computed tomography images analysis using image processing: A Review

<span lang="EN-US">Stroke is the second-leading cause of death globally; therefore, it needs immediate treatment to prevent the brain from damage. Neuroimaging technique for stroke detection such as computed tomography (CT) has been widely used for emergency setting that can provide precise information on an obvious difference between white and gray matter. CT is the comprehensively utilized medical imaging technology for bone, soft tissue, and blood vessels imaging. A fully automatic segmentation became a significant contribution to help neuroradiologists achieve fast and accurate interpretation based on the region of interest (ROI). This review paper aims to identify, critically appraise, and summarize the evidence of the relevant studies needed by researchers. Systematic literature review (SLR) is the most efficient way to obtain reliable and valid conclusions as well as to reduce mistakes. Throughout the entire review process, it has been observed that the segmentation techniques such as fuzzy C-mean, thresholding, region growing, k-means, and watershed segmentation techniques were regularly used by researchers to segment CT scan images. This review is also impactful in identifying the best automated segmentation technique to evaluate brain stroke and is expected to contribute new information in the area of stroke research.</span>

Longitudinal changes in complete avascular area assessed using anterior segmental optical coherence tomography angiography in filtering trabeculectomy bleb

Optical coherence tomography angiography (OCTA) is a new technique for non-invasive imaging of blood vessels, allowing combined evaluation of both deep and surface vessels. The purpose of this study was to evaluate the post-trabeculectomy longitudinal changes in complete avascular area (CAA) of filtering blebs using anterior segment (AS-) OCTA and their association with surgical outcomes. This study included 57 eyes of 53 patients who had undergone trabeculectomy with mitomycin C. AS-OCTA images of filtering bleb were acquired at 3 and 6 months after trabeculectomy, and at 1 month in possible cases. CAAs, regions where complete blood flow was not depicted in AS-OCTA images, were evaluated for their presence, extent, and change over time. CAAs were detected in 37 eyes (65%) and 33 eyes (58%) at 3 and 6 months postoperatively, respectively. The extent of CAAs reduced over time after surgery in most cases. No parameters related to CAAs were significantly associated with surgical success (i.e., intraocular pressure (IOP) ≤ 12 mmHg and IOP reduction > 20% without medication). In conclusion, although it is difficult to predict surgical success by CAA itself, AS-OCTA may be useful for the objective evaluation of the vascularity of filtering blebs.

Retinal biometric identification using convolutional neural network

Authentication is needed to enhance and protect the system from vulnerabilities or weaknesses of the system. There are still many weaknesses in the use of traditional authentication methods such as PINs or passwords, such as being hacked. New methods such as system biometrics are used to deal with this problem. Biometric characteristics using retinal identification are unique and difficult to manipulate compared to other biometric characteristics such as iris or fingerprints because they are located behind the human eye thus they are difficult to reach by normal human vision. This study uses the characteristics of the retinal fundus image blood vessels that have been segmented for its features. The dataset used is sourced from the DRIVE dataset. The preprocessing stage is used to extract its features to produce an image of retinal blood vessel segmentation. The image resulting from the segmentation is carried out with a two-dimensional image transformation such as the process of rotation, enlargement, shifting, cutting, and reversing to increase the quantity of the sample of the retinal blood vessel segmentation image. The results of the image transformation resulted in 189 images divided with the details of the ratio of 80 % or 151 images as training data and 20 % or 38 images as validation data. The process of forming this research model uses the Convolutional Neural Network method. The model built during the training consists of 10 iterations and produces a model accuracy value of 98 %. The results of the model's accuracy value are used for the process of identifying individual retinas in the retinal biometric system.

Real-Time Cerebral Vessel Segmentation in Laser Speckle Contrast Image Based on Unsupervised Domain Adaptation.

Laser speckle contrast imaging (LSCI) is a full-field, high spatiotemporal resolution and low-cost optical technique for measuring blood flow, which has been successfully used for neurovascular imaging. However, due to the low signal–noise ratio and the relatively small sizes, segmenting the cerebral vessels in LSCI has always been a technical challenge. Recently, deep learning has shown its advantages in vascular segmentation. Nonetheless, ground truth by manual labeling is usually required for training the network, which makes it difficult to implement in practice. In this manuscript, we proposed a deep learning-based method for real-time cerebral vessel segmentation of LSCI without ground truth labels, which could be further integrated into intraoperative blood vessel imaging system. Synthetic LSCI images were obtained with a synthesis network from LSCI images and public labeled dataset of Digital Retinal Images for Vessel Extraction, which were then used to train the segmentation network. Using matching strategies to reduce the size discrepancy between retinal images and laser speckle contrast images, we could further significantly improve image synthesis and segmentation performance. In the testing LSCI images of rodent cerebral vessels, the proposed method resulted in a dice similarity coefficient of over 75%.

Functional imaging of human retina using integrated multispectral and laser speckle contrast imaging.

A novel integration of retinal multispectral imaging (MSI), retinal oximetry and laser speckle contrast imaging (LSCI) is presented for functional imaging of retinal blood vessels that could potentially allow early detection or monitoring of functional changes. We designed and built a cost-effective, scalable, retinal imaging instrument that integrates structural and functional retinal imaging techniques, including MSI, retinal oximetry and LSCI. Color fundus imaging was performed with 470 nm, 550 nm and 600 nm wavelength light emitting diode (LED) illumination. Retinal oximetry was performed using 550 nm and 600 nm LED illumination. LSCI of blood flow was performed using 850 nm laser diode illumination at 82 frames per second. LSCI can visualize retinal and choroidal vasculature without requiring exogenous contrast agents and can provide time-resolved information on blood flow, generating a cardiac pulse waveform from retinal vasculature. The technology can rapidly acquire structural MSI images, retinal oximetry and LSCI blood flow information in a simplified clinical workflow without requiring patients to move between instruments. Results from multiple modalities can be combined and registered to provide structural as well as functional information on the retina. These advances can reduce barriers for clinical adoption, accelerating research using MSI, retinal oximetry and LSCI of blood flow for diagnosis, monitoring and elucidating disease pathogenesis.

A 100-V Withstanding Analog-Front-End for High-Resolution Intravascular Ultrasound Imaging.

Intravascular Ultrasound ultrasonic imaging (IVUS) can microscopically image blood vessels and reveal tissue layers from within the blood vessel lumen. It has high tissue penetration ability for lesion classification and can image through blood. Compared to optical techniques, however, IVUS has lower resolution arising from low acoustic bandwidths which cannot resolve sharp edges. The presented 100-V withstanding Analog-Front-End (AFE) was developed to enable a high resolution, low cost IVUS system using a high-bandwidth focused polymer transducer with 40-MHz center frequency. The fabricated AFE interfaced with the transducer with minimal insertion loss, could withstand and duplex 100-V high voltage pulses and echo signal, and had a total signal chain gain of 9.8 dB. The AFE achieved a signal-to-noise ratio (SNR) of 20.1 dB including the insertion loss of the high-impedance transducer. AFE SNR was limited by input impedance required for high-voltage pulse clamping circuitry, but was sufficient for IVUS echo reception.Clinical Relevance- This work has the potential to enable much higher resolution, and potentially cheaper, IVUS imaging in blood vessels by integrating low-cost acoustic transducers with interface amplifiers directly on the catheter.

Noise-optimized virtual monoenergetic imaging technology of the third-generation dual-source computed tomography and its clinical applications.

The third-generation dual-source computed tomography (DSCT) is among the most advanced imaging methods. It employs noise-optimized virtual monoenergetic imaging (VMI+) technology. It uses the frequency-split method to extract high-contrast image information from low-energy images and low-noise information from images reconstructed at an optimal energy level, combining them to obtain the final image with improved quality. This review is the first to summarize the results of clinical studies that primarily and recently evaluated the VMI+ technique based on tumor, blood vessel, and other lesion classification. We aim to assist radiologists in quickly selecting the appropriate energy level when performing image reconstruction for superior image quality in clinical work and providing several ideas for future scientific research of the VMI+ technique. Presently, VMI+ reconstruction is mostly used for images of various tumors or blood vessels, including coronary plaques, coronary stents, deep vein thromboses, pulmonary embolisms (PEs), active arterial hemorrhages, and endoleaks after endovascular aneurysm repair. In addition, VMI+ has been used for imaging children's heads, liver lesions, pancreatic lacerations, and reducing metal artifacts. Regarding the reconstruction at the optimal energy level, the VMI+ technique yielded a higher image quality than the pre-optimized virtual monoenergetic imaging (VMI) technique and single-energy CT. Moreover, either low concentrations of contrast medium or low iodine injection rates can be applied before VMI+ reconstruction at a low-energy level to reduce contrast agent-related kidney injury risk. After reconstructing an image at the optimal energy level, both the image's window width and level can also be adjusted to improve the image effect's reach and diagnosis suitability. To improve image quality and lesion-imaging clarity and reduce the use of contrast agents, VMI+ reconstruction technology has been applied clinically, in which the selection of energy level is the key to the whole reconstruction process. Our review summarizes these optimal levels for radiologists' reference and suggests new ideas for the direction of future VMI+ research.

A Generalized Approach for Automatic 3-D Geometry Assessment of Blood Vessels in Transverse Ultrasound Images Using Convolutional Neural Networks.

Accurate 3-D geometries of arteries and veins are important clinical data for diagnosis of arterial disease and intervention planning. Automatic segmentation of vessels in the transverse view suffers from the low lateral resolution and contrast. Convolutional neural networks are a promising tool for automatic segmentation of medical images, outperforming the traditional segmentation methods with high robustness. In this study, we aim to create a general, robust, and accurate method to segment the lumen-wall boundary of healthy central and peripheral vessels in large field-of-view freehand ultrasound (US) datasets. Data were acquired using the freehand US, in combination with a probe tracker. A total of ±36 000 cross-sectional images, acquired in the common, internal, and external carotid artery ( N = 37 ), in the radial, ulnar artery, and cephalic vein ( N = 12 ), and in the femoral artery ( N = 5 ) were included. To create masks (of the lumen) for training data, a conventional automatic segmentation method was used. The neural networks were trained on: 1) data of all vessels and 2) the carotid artery only. The performance was compared and tested using an open-access dataset. The recall, precision, DICE, and intersection over union (IoU) were calculated. Overall, segmentation was successful in the carotid and peripheral arteries. The Multires U-net architecture performs best overall with DICE = 0.93 when trained on the total dataset. Future studies will focus on the inclusion of vascular pathologies.

Comparison Between Median Filter and Wiener Filter to Get High Accuracy for Blood Vessel Image Extraction

With today's advancing technology, support of developing hardware and software systems, the developments in the field of medicine have increased considerably. In particular, medical image analysis and processing systems have taken a considerable lead. The development of an automatic system could provide great convenience for doctors and practitioners in the field. The image processing techniques proposed in this study can contribute to more effective analysis and more accurate diagnosis, regardless of the individual levels of experience of the users or particular situations and conditions such as fatigue or image quality. This paper presents a robust method for retinal blood vessel segmentation and some automatic algorithms for analyzing the vessel network and pixel classification into vessel and non-vessel .The aim of the work extraction or segmentation of retinal blood vessels used computer vision and image processing for getting high accuracy (comparison with manual) .Also We used the preprocessing techniques for enhancement of the image .And used two types of filter for comparing result to get best scenario. Also for simulation result we used the matlab and implement on DRIVE database.

Ensemble of Deep Learning Cascades for Segmentation of Blood Vessels in Confocal Microscopy Images.

Detection, segmentation, and quantification of microvascular structures are the main steps towards studying microvascular remodeling. Combined with appropriate staining, confocal microscopy imaging enables exploration of the full 3D anatomical characteristics of microvascular systems. Segmentation of confocal microscopy images is a challenging task due to complexity of anatomical structures, staining and imaging issues, and lack of annotated training data. In this paper, we propose a deep learning system for robust segmentation of cranial vasculature of mice in confocal microscopy images. The proposed system is an ensemble of two deep-learning cascades consisting of two coarse-to-fine subnetworks with skip connections in between. One cascade aims to improve sensitivity, while the other aims to improve precision of the segmentation results. Our experiments on mice cranial vasculature showed promising results achieving segmentation accuracy of 92.02% and dice score of 81.45% despite being trained on very limited confocal microscopy data.

Depth resolution in multifocus laser speckle contrast imaging.

Laser speckle contrast imaging (LSCI) can be used to evaluate blood flow based on spatial or temporal speckle statistics, but its accuracy is undermined by out-of-focus image blur. In this Letter, we show how the fraction of dynamic versus static light scattering is dependent on focus, and describe a deconvolution strategy to correct for out-of-focus blur. With the aid of a z-splitter, which enables instantaneous multifocus imaging, we demonstrate depth-resolved LSCI that can robustly extract multi-plane structural and flow-speed information simultaneously. This method is applied to in vivo imaging of blood vessels in a mouse cortex and provides improved estimates of blood flow speed throughout a depth range of 300µm.

Analysis of Glomerular Filtration Rate in Ischemic Cerebrovascular Diseases under the Magnetic Resonance Angiography Image Segmentation Algorithm

In order to discuss the segmentation effect of the magnetic resonance angiography (MRA) image segmentation algorithm based on the fuzzy clustering algorithm and DR-CV model and the prognostic value of glomerular filtration rate (GFR) in the ischemic cerebrovascular disease (ICVD), a total of 178 patients who were admitted to the hospital and received MRA due to ICVD were selected as the research objects of this study. Blood vessel segmentation was performed on the MRA image by fuzzy clustering algorithm and DR-CV model, and all patients were divided into a control group (group A), a single-vessel stenosis group (group B), a two-vessel stenosis group (group C), and a multiple-vessel stenosis group (group D). The GFR was estimated by using the dietary modification equation for kidney disease, and the correlation between GFR and the severity of arterial stenosis in patients with ICVD was analyzed. It was found that the results of the Dice similarity index (DSI) of the MRA image blood vessel segmentation algorithm based on the fuzzy clustering algorithm and the integrated model of boundary and regional information (DR-CV model) were all above 85%. The age and GFR values of the four groups of patients were significantly different ( P < 0.05). The proportions of patients in groups C and D in the group with low DFR were significantly different from those in groups A and B ( P < 0.01); the proportions of patients in groups A and B in the high-level GFR group had extremely significant differences compared with group D ( P < 0.01). Age, GFR, total cholesterol (TC), and high-density lipoprotein-C (HDL-C) were correlated with the degree of arterial stenosis ( P < 0.05). It showed that the segmentation effect of MRA image blood vessel segmentation algorithm based on the fuzzy clustering algorithm and DR-CV model was better, and the GFR level can be used as an independent risk factor for the ICVD.

Integration of PEG-conjugated gadolinium complex and superparamagnetic iron oxide nanoparticles as T 1-T 2 dual-mode magnetic resonance imaging probes.

The T1−T2 dual-mode probes for magnetic resonance imaging (MRI) can non-invasively acquire comprehensive information of different tissues or generate self-complementary information of the same tissue at the same time, making MRI a more flexible imaging modality for complicated applications. In this work, three Gadolinium-diethylene-triaminepentaaceticacid (Gd-DTPA) complex conjugated superparamagnetic iron oxide (SPIO) nanoparticles with different Gd/Fe molar ratio (0.94, 1.28 and 1.67) were prepared as T1–T2 dual-mode MRI probes, named as SPIO@PEG-GdDTPA0.94, SPIO@PEG-GdDTPA1.28 and SPIO@PEG-GdDTPA1.67, respectively. All SPIO@PEG-GdDTPA nanocomposites with 8 nm spherical SPIO nanocrystals showed good Gd3+ chelate stability. SPIO@PEG-GdDTPA0.94 nanocomposites with lowest Gd/Fe molar ratio show no cytotoxicity to Raw 264.7 cells as compared to SPIO@PEG-GdDTPA1.28 and SPIO@PEG-GdDTPA1.67. SPIO@PEG-GdDTPA0.94 nanocomposites with r1 (8.4 mM−1s−1), r2 (83.2 mM−1s−1) and relatively ideal r2/r1 ratio (9.9) were selected for T1–T2 dual-mode MRI of blood vessels and liver tissue in vivo. Good contrast images were obtained for both cardiovascular system and liver in animal studies under a clinical 3 T scanner. Importantly, one can get high-quality contrast-enhanced blood vessel images within the first 2 h after contrast agent administration and acquire liver tissue anatomy information up to 24 h. Overall, the strategy of one shot of the dual mode MRI agent could bring numerous benefits not only for patients but also to the radiologists and clinicians, e.g. saving time, lowering side effects and collecting data of different organs sequentially.

Fluorescent Gold Nanoclusters for In Vivo Shortwave-Infrared Imaging

Gold nanoclusters (Au-NCs) are unique fluorescent nanomaterials that can be used for in vitro and in vivo bioimaging. Au-NCs are less toxic, and their size is much smaller than that of other fluorescent nanomaterials such as quantum dots. The emission wavelengths of Au-NCs depend on the nanocluster size, which can be controlled by the reaction conditions. Recently, shortwave-infrared (SWIR) emitting Au-NCs have attracted much attention for bioimaging. SWIR fluorescence (900–1400 nm) enables deep tissue imaging due to the weak absorption and scattering of the SWIR fluorescence by tissues. Since the autofluorescence of tissues is almost zero in the SWIR region, SWIR fluorescence imaging results in high contrast images compared to visible and near-infrared imaging. For the SWIR fluorescence imaging, the development of biocompatible SWIR fluorescent probes is crucial. Although several types of SWIR fluorescent probes based on quantum dots, rare-earth-doped nanoparticles, and organic dyes have been appeared, there are very few examples of SWIR probe which cytotoxicity is low enough to use in biomedical fields. Herein, we present the synthesis, and optical properties of bovine serum albumin (BSA) capped Au-NCs (Au-NCs@BSA), which emit at the SWIR region. We demonstrate the efficacy of Au-NCs@BSA for in vivo SWIR fluorescence imaging of brain vasculatures, blood vessels, lymph nodes and tumors in mice.

Fusing dense and ReZero residual networks for super-resolution of retinal images

• A deep neural network (DRRN) for super-resolution of retinal image is built. • Use dense connections in the feature mapping to reuse feature information. • Exploit local and global residuals learning to recover retinal image details. • Use ReZero to dynamically learn the weight of the residual. Retinal images have important reference value for the analysis of cardiovascular diseases such as diabetes and hypertension. However, because of the large number of artifacts at the end of the blood vessel and the unclearness of the intersection of the blood vessel, it will cause the inaccuracy of the retinal blood vessel segmentation, thereby affecting the diagnosis of retinal diseases. To solve this problem, we proposed dense and ReZero residual networks for super-resolution of retinal images (DRRN). Firstly, we used a dense connection module to obtain more feature information. Then, we added local residual learning and global residual learning respectively to improve the resolution of retinal image blood vessels from coarse to fine. We also introduced ReZero (residual with zero initialization) to the local residual learning, which can dynamically facilitate well-behaved gradients in a deep network with dense connection modules to make the network converge faster. Finally, we used a tandem structure to reconstruct the local details of the retinal image. The results showed that our method can better reconstruct the details of the retinal image, and can accelerate the processing speed on STARE, DRIVE, and CHASE_DB1 datasets.

Photoacoustic computed tomography of mechanical HIFU-induced vascular injury.

Mechanical high-intensity focused ultrasound (HIFU) has been used for cancer treatment and drug delivery. Existing monitoring methods for mechanical HIFU therapies such as MRI and ultrasound imaging often suffer from high cost, poor spatial-temporal resolution, and/or low sensitivity to tissue's hemodynamic changes. Evaluating vascular injury during mechanical HIFU treatment, therefore, remains challenging. Photoacoustic computed tomography (PACT) is a promising tool to meet this need. Intrinsically sensitive to optical absorption, PACT provides high-resolution imaging of blood vessels using hemoglobin as the endogenous contrast. In this study, we have developed an integrated HIFU-PACT system for detecting vascular rupture in mechanical HIFU treatment. We have demonstrated singular value decomposition for enhancing hemorrhage detection. We have validated the HIFU-PACT performance on phantoms and in vivo animal tumor models. We expect that PACT-HIFU will find practical applications in oncology research using small animal models.

A Near‐Infrared Organic Fluorescent Probe for Broad Applications for Blood Vessels Imaging by High‐Throughput Screening via 3D‐Blood Vessel Models (Small Methods 8/2021)

Flexoelectric Effect In article number 2100338, Matsusaki and co-workers report the discovery of an NIR blood vessel organic probe by screening a diversity-oriented fluorescence library through an engineered three-dimensional blood capillary-based high-throughput system. This physiologically reliable screening system showed stable and strong adsorption to the mouse endothelium post-injection.

Automated High-Resolution Structure Analysis of Plant Root with a Morphological Image Filtering Algorithm

Research on rice (Oryza sativa) roots demands the automatic analysis of root architecture during image processing. It is challenging for a digital filter to identify the roots from the obscure and cluttered background. The original Frangi algorithm, presented by Alejandro F. Frangi in 1998, is a successful low-pass filter dedicated to blood vessel image enhancement. Considering the similarity between vessels and roots, the Frangi filter algorithm is applied to outline the roots. However, the original Frangi only enhances the tube-like primary roots but erases the lateral roots during filtering. In this paper, an improved Frangi filtering algorithm (IFFA), designed for plant roots, is proposed. Firstly, an automatic root phenotyping system is designed to fulfill the high-throughput root image acquisition. Secondly, multilevel image thresholding, connected components labeling, and width correction are used to optimize the output binary image. Thirdly, to enhance the local structure, the Gaussian filtering operator in the original Frangi is redesigned with a truncated Gaussian kernel, resulting in more discernible lateral roots. Compared to the original Frangi filter and commercially available software, IFFA is faster and more accurate, achieving a pixel accuracy of 97.48%. IFFA is an effective morphological filtering approach to enhance the roots of rice for segmentation and further biological research. It is convincing that IFFA is suitable for different 2-D plant root image processing and morphological analysis.

Segmentasi Citra Pembuluh Darah Retina Menggunakan Operasi Morfologi Iteratif

Retinal vessel segmentation is part of the morphological extraction of retinal blood vessels that plays an essential role in medical image processing. Manual segmentation is possible to do, but it is time-consuming and requires special operators. Moreover, the possibility of variability between operators is vast. This study aims to answer the shortcomings of the manual segmentation process by automatically segmenting retinal blood vessels. The main contribution of this study is the use of a simple method to iteratively segment retinal blood vessels. All processes in the segmentation are simulated using Matlab. The algorithm was evaluated by comparing the results of the automatic segmentation with 20 manually segmented images from the STARE dataset. The result show specificity 98.13%, accuracy 93.60%, sensitivity 56.42%, precision 80.48%, and the dice coefficient 64.06%. In conclusion, the automatic retinal blood vessel image segmentation process worked well.