Blood Vessels In Images Research Articles

Ferumoxtran-10-enhanced 3-T Magnetic Resonance Angiography of Pelvic Arteries: Initial Experience

BackgroundPatients with renal impairment cannot undergo angiography because iodine and gadolinium contrast agents are contraindicated. Iron-containing ultrasmall superparamagnetic iron oxide particles, such as ferumoxtran-10, are not contraindicated in these patients. Thus, patients with renal failure can still undergo angiography with ferumoxtran-10. ObjectiveTo evaluate the visibility of pelvic vessels with magnetic resonance angiography (MRA) using ferumoxtran-10 as contrast agent. Design, setting, and participantsThree hundred and eighty-one patients diagnosed with primary or recurrent prostate cancer underwent pelvic ferumoxtran-10 MRA. Eleven anatomical pelvic-vessel segments per patient were evaluated using qualitative and quantitative criteria for image quality (IQ), vessel visibility (VV), and the contrast-to-noise ratio (CNR). InterventionFerumoxtran-10-enhaced MRA. Outcome measurements and statistical analysisIQ, VV, and CNR were assessed on a 5-point scale for each data set/vessel segment (very poor, poor, moderate, good, and excellent). Results and limitationsIQ was good to excellent for 98.2% of the data sets and VV was good to excellent for 97.7% of all vessel segments. The mean CNR for all segments was 88.13 (standard deviation 4.22). Contrast bolus imaging cannot be performed with this technique, so it is impossible to visualize the arterial or venous phase separately. The timing of contrast administration is also a limitation, with MRA performed 1 d after contrast infusion. ConclusionsFerumoxtran-10 MRA showed excellent image quality and visibility for pelvic vessels. In addition, the homogeneity of the intraluminal contrast was superior. Patients with preterminal or terminal renal function can benefit from ferumoxtran-10 MRA if visualization of their pelvic vessels is required. Patient summaryMagnetic resonance imaging of blood vessels using a contrast agent called ferumoxtran-10 is a promising technique for patients with impaired kidney function, as it provides high-quality visualization of blood vessels in the pelvis.

Blood Vessel Detection Algorithm for Tissue Engineering and Quantitative Histology

Immunohistochemistry for vascular network analysis plays a fundamental role in basic science, translational research and clinical practice. However, identifying vascularization in histological tissue images is time consuming and markedly depends on the operator’s experience. In this study, we present “blood vessel detection—BVD”, an automatic algorithm for quantitative analysis of blood vessels in immunohistochemical images. BVD is based on extraction and analysis of low-level image features and spatial filtering techniques, which do not require a training phase. BVD algorithm performance was comparatively evaluated on histological sections from three different in vivo experiments. Collectively, 173 independent images were analyzed, and the algorithm's results were compared to those obtained by human operators. The developed BVD algorithm proved to be a robust and versatile tool, being able to quantify number, area, and spatial distribution of blood vessels within all three considered histologic datasets. BVD is provided as an open-source application working on different operating systems. BVD is supported by a user-friendly graphical interface designed to facilitate large-scale analysis.

A Deep Bottleneck U-Net Combined With Saliency Map For Classifying Diabetic Retinopathy In Fundus Images

Early detection of retinopathy plays an important role in the care of people with diabetes. Classification of diabetic retinopathy in fundus images is very challenging because the blood vessels in the retinal images are too small. Morphology of objects with multi-level saliency is the recent choice because of the activation of feature extraction. However, the challenges of the input models are very complex with the blood. The color, lighting or context can become the reasons that create the decline of the primary key for training. This paper proposes a method for classification of diabetic retinopathy using saliency and shape detection of objects based on a deep Bottleneck U-Net (DbU-Net) and support vector machines in retinal blood vessels. The proposed method includes four stages: preprocessing, feature extraction using DbU-Net, saliency prediction and classification based on the support vector machine. To evaluate this method, its results are compared to the results of the other methods by using the same datasets of STARE and DRIVE for testing with evaluation criteria such as sensitivity, specificity, and accuracy. The accuracy of the proposed method is about 97.1% in these datasets. To assess the levels of diabetes, the diagnostician must initially identify the retinal image with diabetes or not. The result of this paper may help the diagnostician to easily do this.

Improving the Curvelet Saliency and Deep Convolutional Neural Networks for Diabetic Retinopathy Classification in Fundus Images

Retinal vessel images give a wide range of the abnormal pixels of patients. Therefore, classifying the diseases depending on fundus images is a popular approach. This paper proposes a new method to classify diabetic retinopathy in retinal blood vessel images based on curvelet saliency for segmentation. Our approach includes three periods: pre-processing of the quality of input images, calculating the saliency map based on curvelet coefficients, and classifying VGG16. To evaluate the results of the proposed method STARE and HRF datasets are used for testing with the Jaccard Index. The accuracy of the proposed method is about 98.42% and 97.96% with STARE and HRF datasets respectively.

Development of Stereo NIR-II Fluorescence Imaging System for 3D Tumor Vasculature in Small Animals.

Near-infrared-II (NIR-II, 1000–1700 nm) fluorescence imaging boasts high spatial resolution and deep tissue penetration due to low light scattering, reduced photon absorption, and low tissue autofluorescence. NIR-II biological imaging is applied mainly in the noninvasive visualization of blood vessels and tumors in deep tissue. In the study, a stereo NIR-II fluorescence imaging system was developed for acquiring three-dimension (3D) images on tumor vasculature in real-time, on top of the development of fluorescent semiconducting polymer dots (IR-TPE Pdots) with ultra-bright NIR-II fluorescence (1000–1400 nm) and high stability to perform long-term fluorescence imaging. The NIR-II imaging system only consists of one InGaAs camera and a moving stage to simulate left-eye view and right-eye view for the construction of 3D in-depth blood vessel images. The system was validated with blood vessel phantom of tumor-bearing mice and was applied successfully in obtaining 3D blood vessel images with 0.6 mm- and 5 mm-depth resolution and 0.15 mm spatial resolution. The NIR-II stereo vision provides precise 3D information on the tumor microenvironment and blood vessel path.

Bridge-Net: Context-involved U-net with patch-based loss weight mapping for retinal blood vessel segmentation

Retinal blood vessel segmentation in fundus images plays an important role in the early diagnosis and treatment of retinal diseases. In recent years, the segmentation methods based on deep neural networks have attracted the attention of experts and scholars. However, due to the complexity of the distribution of blood vessels in fundus images and the imbalance between blood vessels and background, retinal blood vessel segmentation remains challenging. In this paper, we present a retinal blood vessel segmentation method using deep neural networks. Firstly, we propose a novel deep network architecture named Bridge-net to make use of the context of the retinal blood vessels efficiently. Specifically, the architecture incorporates a recurrent neural network (RNN) into a convolutional neural network (CNN) to deliver the context and then to produce the probability map of the retinal blood vessels. Secondly, we propose a patch-based loss weight mapping by considering the distributions of different types of blood vessels to correct the imbalance, since there are large morphological differences between thick and thin blood vessels. Finally, we evaluate our method on three publicly datasets STARE, DRIVE, and CHASE_DB1, and compare the results to eighteen state-of-the-art approaches. We also compare our method with some existing approaches on a high-resolution dataset, i.e., HRF. The results show that our method achieves better/comparable performances when compared to the existing approaches. The results on various datasets also verify the effectiveness and stability of the proposed method.

Image enhancement in acoustic-resolution photoacoustic microscopy enabled by a novel directional algorithm.

By considering the line pattern of acoustic-resolution photoacoustic microscopy (AR-PAM) vessel images, we develop modified algorithms for synthetic aperture focusing technique (SAFT) and deconvolution based on a directional approach to enhance images. The modified algorithms consist of Fourier accumulation SAFT (FA-SAFT) and directional model-based (D-MB) deconvolution. To evaluate the performance of our algorithms, we conduct a series of imaging experiments and apply our algorithms, and existing SAFT and deconvolution algorithms are also applied for side-by-side comparison. By imaging tungsten wire phantom, our algorithms enable full width at half maximum of 26 - 31 µm over depth of focus of 1.8 mm and minimum resolvable distance of 46 - 49 µm, besting existing SAFT and deconvolution algorithms. Imaging of leaf skeleton phantom and in vivo imaging of mouse blood vessels also prove that our algorithm is capable of providing high-resolution, high-signal-to-noise ratio, and good-fidelity results for complex structures and for in vivo applications, especially for the images with the line pattern. The proposed directional approach can not only be used in AR-PAM but also in other imaging modalities to deal with the line pattern, such as FA-SAFT for ultrasound imaging and D-MB deconvolution for optical coherence tomography angiography.

Fabrication of semiconducting polymer-blend dots with strong near-infrared fluorescence and afterglow luminescence for bioimaging

Fluorescence imaging at longer wavelength and autofluorescence-free imaging were two main strategies to improve the optical imaging performance of traditional near-infrared (NIR) fluorescence imaging, and the design of corresponding imaging agents were heavily explored recently. Herein, polymer-blend dots (PBdots) with strong fluorescence and afterglow luminescence in both NIR-I (700–900 nm) and NIR-II (900–1700 nm) windows were successfully fabricated by encapsulating two functional semiconducting polymers into one nanoparticle. With efficient intra-particle interactions, the PBdots exhibited an afterglow lifetime of 181 s after 730 nm NIR laser activation, which can be activated in vivo. The obtained PBdots with similar emission intensities in NIR-I and NIR-II regions enable fair comparison between different optical imaging modalities, which provides a clear guidance for the choice of suitable imaging modality for different purpose. As the first imaging agents which integrate NIR-II fluorescence imaging and NIR-I afterglow imaging, the PBdots also provide both high resolution and large signal-to-background ratio for blood vessel, lymph node and tumor imaging in living mice.

Creation of Stereo-paired Color Vascular Anatomical Charts Using 3-dimentional Images

Three-dimensional (3D) images of blood vessels in the human body, which are acquired by X-ray computed tomography (CT) and cone-beam CT of Angiography devices, are widely used in medical diagnosis and treatment. Using the 3DCT images of blood vessels, we created stereo-paired color vascular anatomical charts for better understanding of vascular anatomy in clinical settings, patient explanations, and student education. Since it is difficult to distinguish branches of blood vessels that show three-dimensionally complicated running such as cerebral blood vessels, we made it easier to identify them anatomically by color-coding each branch of the blood vessel. Also, by using stereo-paired images, we can see the three-dimensional blood vessel running. In the past anatomical books and vascular anatomy atlas, there was no anatomical chart of the whole body blood vessels that could be color-coded and stereoscopically viewed. We have made it possible to identify blood vessels by the stereoscopic vision of the blood vessels using this stereo-paired color anatomical chart. In addition, this vascular anatomical chart can be additionally revised according to the needs of the clinical and educational settings to be used, and the data can be converted into an electronic file so that it can be easily used in the field of radiological examination or at home through electronic media.

Microscopic retinal blood vessels detection and segmentation using support vector machine and K-nearest neighbors.

The retina is the deepest layer of texture covering the rear of the eye, recorded by fundus images. Vessel detection and segmentation are useful in disease diagnosis. The retina's blood vessels could help diagnose maladies such as glaucoma, diabetic retinopathy, and blood pressure. A mix of supervised and unsupervised strategies exists for the detection and segmentation of blood vessels images. The tree structure of retinal blood vessels, their random area, and different thickness have caused vessel detection difficulties at machine learning calculations. Since the green band of retinal images conveys more information about the vessels, they are utilized for microscopic vessels detection. The current research proposes an administered calculation for segmentation of retinal vessels, where two upgrading stages depending on filtering and comparative histogram were applied after pre-processing and image quality improvement. At that point, statistical features of vessel tracking, maximum curvature and curvelet coefficient are extracted for each pixel. The extracted features are classified by support vector machine and the k-nearest neighbors. The morphological operators then enhance the classified image at the final stage to segment with higher accuracy. The dice coefficient is utilized for the evaluation of the proposed method. The proposed approach is concluded to be better than different strategies with a normal of 92%.

Imaging Blood Vessels and Lymphatics in Mouse Trachea Wholemounts.

Changes in blood vessels and lymphatics in health and disease are easier to understand and interpret when studied microscopically in three dimensions. The mouse trachea is a simple, yet powerful, and versatile model system in which to achieve this. We describe practical immunohistochemical methods for fluorescence and confocal microscopy of wholemounts of the mouse trachea to achieve this purpose in which the entire vasculature can be visualized from the organ level to the cellular and subcellular level. Blood vessels and lymphatics have highly stereotyped vascular architectures that repeat in arcades between the tracheal cartilages. Arterioles, capillaries, and venules can be easily identified for the blood vessels, while the lymphatics consist of initial lymphatics and collecting lymphatics. Even small abnormalities in either blood vessels or lymphatics can be noticed and evaluated in three dimensions. We and others have used the mouse trachea for examining in situ angiogenesis and lymphangiogenesis, vascular development and regression, vessel patency, differences in transgenic mice, and pathological changes, such as increased vascular permeability induced by inflammatory mediators.

A novel TMTP1-modified theranostic nanoplatform for targeted in vivo NIR-II fluorescence imaging-guided chemotherapy for cervical cancer.

Near-infrared II (NIR-II, 900-1700 nm) fluorescence bioimaging with advantages of good biosafety, excellent spatial resolution, high sensitivity, and contrast has attracted great attention in biomedical research fields. However, most of the nanoprobes used for NIR-II fluorescence imaging have poor tumor-targeting ability and therapeutic efficiency. To overcome these limitations, a novel NIR-II-emissive theranostic nanoplatform for fluorescence imaging and treatment of cervical cancer was designed and prepared. The NIR-II-emissive dye IR-783 and chemotherapy drug doxorubicin (DOX) were encapsulated into liposomes, and the tumor-targeting peptide TMTP1 (a polypeptide with a sequence of cyclic ASN Val Val Arg Gln Cys) was conjugated to the surface of the liposomes to form IR-783-DOX-TMTP1 nanoparticles (NPs) via self-assembly methods. The IR-783-DOX-TMTP1 NPs showed strong NIR-II emission, excellent biocompatibility and a long lifetime in vivo. Furthermore, high-definition NIR-II fluorescence microscopy images of ear blood vessels and intratumoral blood vessels were obtained from IR-783-DOX-TMTP1 NP-stained mice with high spatial resolution under 808 nm laser excitation. Moreover, IR-783-DOX-TMTP1 NPs showed strong tumor-targeting ability and highly efficient chemotherapeutic characteristics towards cervical tumors. The novel targeting and NIR-II-emissive IR-783-DOX-TMTP1 NPs have great potential in diagnosis and therapy for cervical cancer.

Vgg-Bridged Dropout Dense U-Net for Robust Segmentation of Retinal Blood Vessel Image

Vgg-Bridged Dropout Dense U-Net for Robust Segmentation of Retinal Blood Vessel Image

Hybrid Registration Method of Narrow-Band Blood Vessel Image for Vascular Targeted Photodynamic Therapy

Hybrid Registration Method of Narrow-Band Blood Vessel Image for Vascular Targeted Photodynamic Therapy

VG-DropDNet a Robust Architecture for Blood Vessels Segmentation on Retinal Image

Additional layers to the U-Net architecture leads to additional parameters and network complexity. The Visual Geometry Group (VGG) architecture with 16 backbones can overcome the problem with small convolutions. Dense Connected (DenseNet) can be used to avoid excessive feature learning in VGG by directly connecting each layer using input from the previous feature map. Adding a Dropout layer can protect DenseNet from Overfitting problems. This study proposes a VG-DropDNet architecture that combines VGG, DenseNet, and U-Net with a dropout layer in blood vessels retinal segmentation. VG-DropDNet is applied to Digital Retina Image for Vessel Extraction (DRIVE) and Retina Structured Analysis (STARE) datasets. The results on DRIVE give great accuracy of 95.36%, sensitivity of 79.74% and specificity of 97.61%. The F1-score on DRIVE of 0.8144 indicates that VG-DropDNet has great precision and recall. The IoU result is 68.70. It concludes that the resulting image of VG-DropDNet has a great resemblance to its ground truth. The results on STARE are excellent for accuracy of 98.56%, sensitivity of 91.24%, specificity of 92.99% and IoU of 86.90%. The results of the VGG-DropDNet on STARE show that the proposed method is excellent and robust for blood vessels retinal segmentation. The Cohen’s Kappa coefficient obtained by VG-DropDNet at DRIVe is 0.8386 and at STARE is 0.98, it explains that the VG-DropDNet results are consistent and precise in both datasets. The results on various datasets indicate that VG-DropDnet is effective, robust and stable in retinal image blood vessel segmentation.

A Class of Biocompatible Dye-Protein Complex Optical Nanoprobes.

Molecular organic dyes are classic fluorescent nanoprobes finding tremendous uses in biological and life sciences. Yet, they suffer from low brightness, poor photostability, and lack of functional groups for bioconjugation. Here, we describe a class of biocompatible dye-protein optical nanoprobes, which show long-time photostability, superbrightness, and enriched functional groups. These nanoprobes utilize apoferritin (an intracellular protein for iron stores and release) to encase appropriate molecular organic dyes to produce on-demand fluorescence in aqueous solution. A pH-driven dissociation-reconstitution process of apoferritin subunits allows substantial incorporation of hydrophilic (aggregation caused quenching, ACQ) or hydrophobic (aggregation induced enhancement, AIE) dye molecules into the protein nanocavity (8 nm), producing monodispersed dye-apoferritin nanoparticles (apo-dye-NPs, ∼12 nm). As compared with single dye monomer, single apo-dye-NPs possess hundreds of times larger molar extinction coefficient and 2 orders of magnitude higher absolute luminescence quantum yield (up to 45-fold), multiplying fluorescence brightness up to 2778-fold. We show that varying the type of incorporated dyes entails a precise control over nanoprobe emission profile tunable in a broad spectral range of 370-1300 nm. Mechanical investigations indicate that the diversified microstructures of nanocavity inner surface are able to conform ACQ dyes at reasonable space interval while providing protein-guided-stacking for AIE dyes, thus enhancing fluorescence quantum yield through confining intermolecular quenching and intramolecular rotation. Moreover, apo-dye-NPs are able to emit stable fluorescence (over 13 min) without quenching in confocal imaging of HepG2 cancer cell under ultrahigh laser irradiance (1.3 × 106 W/cm2). These superb properties make them suitable, as demonstrated in this work, for long-term super-resolved structured illumination microscopic cell imaging (spatial resolution, 117 nm) over 48 h, near-infrared (NIR) fluorescence angiography imaging of whole-body blood vessels (spatial resolution, 380 μm), and NIR photoacoustic imaging of liver in vivo.

Quantum dots assisted in vivo two-photon microscopy with NIR-II emission

With the advantages of high resolution and deep penetration depth, two-photon excited NIR-II (900–1880 nm) fluorescence (2PF) microscopic bioimaging is promising. However, due to the lack of imaging systems and suitable probes, few such works, to our best knowledge, were demonstrated utilizing NIR-II excitation and NIR-II fluorescence simultaneously. Herein, we used aqueously dispersible PbS/CdS quantum dots with bright NIR-II fluorescence as the contrast agents. Under the excitation of a 1550 nm femtosecond (fs) laser, they emitted bright 2PF in the NIR-II region. Moreover, a 2PF lifetime imaging microscopic (2PFLIM) system was implemented, and in vivo 2PFLIM images of mouse brain blood vessels were obtained for the first time to our best knowledge. To improve imaging speed, an in vivo two-photon fluorescence microscopy (2PFM) system based on an InGaAs camera was implemented, and in vivo 2PFM images of QDs-stained mouse brain blood vessels were obtained.

Multi-step deep neural network for identifying subfascial vessels in a dorsal skinfold window chamber model.

Automatic segmentation of blood vessels in the dorsal skinfold window chamber (DWSC) model is a prerequisite for the evaluation of vascular-targeted photodynamic therapy (V-PDT) biological response. Recently, deep learning methods have been widely applied in blood vessel segmentation, but they have difficulty precisely identifying the subfascial vessels. This study proposed a multi-step deep neural network, named the global attention-Xnet (GA-Xnet) model, to precisely segment subfascial vessels in the DSWC model. We first used Hough transform combined with a U-Net model to extract circular regions of interest for image processing. GA step was then employed to obtain global feature learning followed by coarse segmentation for the entire blood vessel image. Secondly, the coarse segmentation of blood vessel images from the GA step and the same number of retinal images from the DRIVE datasets were combined as the mixing sample, inputted into the Xnet step to learn the multiscale feature predicting fine segmentation maps of blood vessels. The data show that the accuracy, sensitivity, and specificity for the segmentation of multiscale blood vessels in the DSWC model are 96.00%, 86.27%, 96.47%, respectively. As a result, the subfascial vessels could be accurately identified, and the connectedness of the vessel skeleton is well preserved. These findings suggest that the proposed multi-step deep neural network helps evaluate the short-term vascular responses in V-PDT.

Vessel-GAN: Angiographic reconstructions from myocardial CT perfusion with explainable generative adversarial networks

Dynamic CT angiography derived from CT perfusion data can obviate a separate coronary CT angiography and the use of ionizing radiation and contrast agent, thereby enhancing patient safety. However, the image quality of dynamic CT angiography is inferior to standard CT angiography images in many studies. This paper proposes an explainable generative adversarial network named vessel-GAN, which resorts to explainable knowledge-based artificial intelligence to perform image translation with increased trustworthiness. Specifically, we design a loss term to better learn the representations of blood vessels in CT angiography images. The loss term based on expert knowledge guides the generator to focus its training on the important features predicted by the discriminator. Additionally, we propose a generator architecture that effectively fuses spatio-temporal representations and further enhances temporal consistency, thereby improving the quality of the generated CT angiography images. The experiment is conducted on a dataset consisting of 232 patients with suspected coronary artery stenosis. Experimental results show that the PSNR value of vessel-GAN is 28.32 dB, SSIM value is 0.91 and MAE value is 47.36. To validate the effectiveness of the proposed synthesis method, we compare that with other image translation frameworks and GAN-based methods. Compared to other image translation methods, the proposed method vessel-GAN can generate more clearly visible blood vessels from source perfusion images. The CTA images generated by vessel-GAN are closer to the real CTA due to the use of adversarial learning. Compared with other GAN-based methods, vessel-GAN can produce sharper and more homogeneous outputs, including realistic vascular structures. The experiment demonstrates that the explainable generative adversarial network has superior performance for it can better control how models learn. Overall, the CT angiography images generated by vessel-GAN can potentially replace a separate standard CT angiography, allowing the possibility of “one-stop” cardiac examination for high-risk coronary artery disease patients who need assessment of myocardial ischemia.

Multiple Multi-Scale Neural Networks Knowledge Transfer and Integration for Accurate Pixel-Level Retinal Blood Vessel Segmentation

Retinal blood vessel segmentation plays an important role for analysis of retinal diseases, such as diabetic retinopathy and glaucoma. However, retinal blood vessel segmentation remains a challenging task due to the low contrast between some vessels and background, the different presenting conditions caused by uneven illumination and the artificial segmentation results are influenced by human experience, which seriously affects the classification accuracy. To address this problem, we propose a multiple multi-scale neural networks knowledge transfer and integration method in order to accurately segment for retinal blood vessel image. With the integration of multi-scale networks and multi-scale input patches, the blood vessel segmentation performance is obviously improved. In addition, applying knowledge transfer to the network training process, the pre-trained network reduces the number of network training iterations. The experimental results on the DRIVE dataset and the CHASE_DB1 dataset show the effectiveness of the method, whose average accuracy on the two datasets are 96.74% and 97.38%, respectively.