Vessel Segmentation Algorithm Research Articles

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.

UNet retinal blood vessel segmentation algorithm based on improved pyramid pooling method and attention mechanism

The segmentation results of retinal vessels have a significant impact on the automatic diagnosis of retinal diabetes, hypertension, cardiovascular and cerebrovascular diseases and other ophthalmic diseases. In order to improve the performance of blood vessels segmentation, a pyramid scene parseing U-Net segmentation algorithm based on attention mechanism was proposed. The modified PSP-Net pyramid pooling module is introduced on the basis of U-Net network, which aggregates the context information of different regions so as to improve the ability of obtaining global information. At the same time, attention mechanism was introduced in the skip connection part of U-Net network, which makes the integration of low-level features and high-level semantic features more efficient and reduces the loss of feature information through nonlinear connection mode. The sensitivity, specificity, accuracy and AUC of DRIVE and CHASE_DB1 data sets are 0.7814, 0.9810, 0.9556, 0.9780; 0.8195, 0.9727, 0.9590, 0.9784. Experimental results show that the PSP-UNet segmentation algorithm based on the attention mechanism enhances the detection ability of blood vessel pixels, suppresses the interference of irrelevant information and improves the network segmentation performance, which is superior to U-Net algorithm and some mainstream retinal vascular segmentation algorithms at present.

Densely connected U-Net retinal vessel segmentation algorithm based on multi-scale feature convolution extraction.

The segmentation results of retinal blood vessels have a significant impact on the automatic diagnosis of various ophthalmic diseases. In order to further improve the segmentation accuracy of retinal vessels, we propose an improved algorithm based on multiscale vessel detection, which extracts features through densely connected networks and reuses features. A parallel fusion and serial embedding multiscale feature dense connection U-Net structure are designed. In the parallel fusion method, features of the input images are extracted for Inception multiscale convolution and dense block convolution, respectively, and then the features are fused and input into the subsequent network. In serial embedding mode, the Inception multiscale convolution structure is embedded in the dense connection network module, and then the dense connection structure is used to replace the classical convolution block in the U-Net network encoder part, so as to achieve multiscale feature extraction and efficient utilization of complex structure vessels and thereby improve the network segmentation performance. The experimental analysis on the standard DRIVE and CHASE_DB1 databases shows that the sensitivity, specificity, accuracy, and AUC of the parallel fusion and serial embedding methods reach 0.7854, 0.9813, 0.9563, 0.9794; 0.7876, 0.9811, 0.9565, 0.9793 and 0.8110, 0.9737, 0.9547, 0.9667; 0.8113, 0.9717, 0.9574, 0.9750, respectively. The experimental results show that multiscale feature detection and feature dense connection can effectively enhance the network model's ability to detect blood vessels and improve the network segmentation performance, which is superior to U-Netalgorithm and some mainstream retinal blood vessel segmentation algorithms at present.

Multichannel Retinal Blood Vessel Segmentation Based on the Combination of Matched Filter and U-Net Network.

Aiming at the current problem of insufficient extraction of small retinal blood vessels, we propose a retinal blood vessel segmentation algorithm that combines supervised learning and unsupervised learning algorithms. In this study, we use a multiscale matched filter with vessel enhancement capability and a U-Net model with a coding and decoding network structure. Three channels are used to extract vessel features separately, and finally, the segmentation results of the three channels are merged. The algorithm proposed in this paper has been verified and evaluated on the DRIVE, STARE, and CHASE_DB1 datasets. The experimental results show that the proposed algorithm can segment small blood vessels better than most other methods. We conclude that our algorithm has reached 0.8745, 0.8903, and 0.8916 on the three datasets in the sensitivity metric, respectively, which is nearly 0.1 higher than other existing methods.

Automatic pulmonary vessel segmentation on noncontrast chest CT: deep learning algorithm developed using spatiotemporally matched virtual noncontrast images and low-keV contrast-enhanced vessel maps.

ObjectivesTo develop a deep learning–based pulmonary vessel segmentation algorithm (DLVS) from noncontrast chest CT and to investigate its clinical implications in assessing vascular remodeling of chronic obstructive lung disease (COPD) patients.MethodsFor development, 104 pulmonary CT angiography scans (49,054 slices) using a dual-source CT were collected, and spatiotemporally matched virtual noncontrast and 50-keV images were generated. Vessel maps were extracted from the 50-keV images. The 3-dimensional U-Net-based DLVS was trained to segment pulmonary vessels (with a vessel map as the output) from virtual noncontrast images (as the input). For external validation, vendor-independent noncontrast CT images (n = 14) and the VESSEL 12 challenge open dataset (n = 3) were used. For each case, 200 points were selected including 20 intra-lesional points, and the probability value for each point was extracted. For clinical validation, we included 281 COPD patients with low-dose noncontrast CTs. The DLVS-calculated volume of vessels with a cross-sectional area < 5 mm2 (PVV5) and the PVV5 divided by total vessel volume (%PVV5) were measured.ResultsDLVS correctly segmented 99.1% of the intravascular points (1,387/1,400) and 93.1% of the extravascular points (1,309/1,400). The areas-under-the receiver-operating characteristic curve (AUROCs) were 0.977 and 0.969 for the two external validation datasets. For the COPD patients, both PPV5 and %PPV5 successfully differentiated severe patients whose FEV1 < 50 (AUROCs; 0.715 and 0.804) and were significantly correlated with the emphysema index (Ps < .05).ConclusionsDLVS successfully segmented pulmonary vessels on noncontrast chest CT by utilizing spatiotemporally matched 50-keV images from a dual-source CT scanner and showed promising clinical applicability in COPD.Key Points • We developed a deep learning pulmonary vessel segmentation algorithm using virtual noncontrast images and 50-keV enhanced images produced by a dual-source CT scanner. • Our algorithm successfully segmented vessels on diseased lungs. • Our algorithm showed promising results in assessing the loss of small vessel density in COPD patients. Supplementary InformationThe online version contains supplementary material available at 10.1007/s00330-021-08036-z.

Two-step machine learning method for the rapid analysis of microvascular flow in intravital video microscopy

Microvascular blood flow is crucial for tissue and organ function and is often severely affected by diseases. Therefore, investigating the microvasculature under different pathological circumstances is essential to understand the role of the microcirculation in health and sickness. Microvascular blood flow is generally investigated with Intravital Video Microscopy (IVM), and the captured images are stored on a computer for later off-line analysis. The analysis of these images is a manual and challenging process, evaluating experiments very time consuming and susceptible to human error. Since more advanced digital cameras are used in IVM, the experimental data volume will also increase significantly. This study presents a new two-step image processing algorithm that uses a trained Convolutional Neural Network (CNN) to functionally analyze IVM microscopic images without the need for manual analysis. While the first step uses a modified vessel segmentation algorithm to extract the location of vessel-like structures, the second step uses a 3D-CNN to assess whether the vessel-like structures have blood flowing in it or not. We demonstrate that our two-step algorithm can efficiently analyze IVM image data with high accuracy (83%). To our knowledge, this is the first application of machine learning for the functional analysis of microvascular blood flow in vivo.

Assessing fairness in performance evaluation of publicly available retinal blood vessel segmentation algorithms

In the literature, various algorithms have been proposed for automatically extracting blood vessels from retinal images. In general, they are developed and evaluated using several publicly available datasets such as the DRIVE and STARE datasets. For performance evaluation, several metrics such as Sensitivity, Specificity, and Accuracy have been widely used. However, not all methods in the literature have been fairly evaluated and compared among their counterparts. In particular, for some publicly available algorithms, the performance is measured only for the area inside the field of view (FOV) of each retinal image while the rest use the complete image for the performance evaluation. Therefore, performing a comparison of the performance of methods in the latter group with those in the former group may lead to a misleading justification. This study aims to assess fairness in the performance evaluation of various publicly available retinal blood vessel segmentation algorithms. The conducted study allows getting several meaningful results: (i) a guideline to assess fairness in performance evaluation of retinal vessel segmentation algorithms, (ii) a more proper performance comparison of retinal vessel segmentation algorithms in the literature, and (iii) a suggestion regarding the use of performance evaluation metrics that will not lead to misleading comparison and justification.

Retinal Vessel Segmentation based on Convolutional Neural Network and Connection Domain Detection

We propose a retinal vessel segmentation algorithm based on a convolution neural network and connected domain detection. After defining the discriminant matrix, we constructed and trained a convolutional neural network model, which can realize the mapping relationship from eye fundus grayscale to the discriminant matrix. This model achieves the preliminary segmentation of retinal vessels. The prediction of uncertain pixels is revised by using the geometric characteristics of the vessels and through the analysis of connected regions. The experimental results show good generalization ability, the average segmentation accuracy, specificity, and sensitivity are 96.64%, 97.96%, and 80.68%, respectively.

ELEMENT: Multi-Modal Retinal Vessel Segmentation Based on a Coupled Region Growing and Machine Learning Approach.

Vascular structures in the retina contain important information for the detection and analysis of ocular diseases, including age-related macular degeneration, diabetic retinopathy and glaucoma. Commonly used modalities in diagnosis of these diseases are fundus photography, scanning laser ophthalmoscope (SLO) and fluorescein angiography (FA). Typically, retinal vessel segmentation is carried out either manually or interactively, which makes it time consuming and prone to human errors. In this research, we propose a new multi-modal framework for vessel segmentation called ELEMENT (vEsseL sEgmentation using Machine lEarning and coNnecTivity). This framework consists of feature extraction and pixel-based classification using region growing and machine learning. The proposed features capture complementary evidence based on grey level and vessel connectivity properties. The latter information is seamlessly propagated through the pixels at the classification phase. ELEMENT reduces inconsistencies and speeds up the segmentation throughput. We analyze and compare the performance of the proposed approach against state-of-the-art vessel segmentation algorithms in three major groups of experiments, for each of the ocular modalities. Our method produced higher overall performance, with an overall accuracy of 97.40%, compared to 25 of the 26 state-of-the-art approaches, including six works based on deep learning, evaluated on the widely known DRIVE fundus image dataset. In the case of the STARE, CHASE-DB, VAMPIRE FA, IOSTAR SLO and RC-SLO datasets, the proposed framework outperformed all of the state-of-the-art methods with accuracies of 98.27%, 97.78%, 98.34%, 98.04% and 98.35%, respectively.

A three-stage shearlet-based algorithm for vessel segmentation in medical imaging

Dictionaries are known tools used in different branches of image processing like edge detection, inpainting and, etc. Segmentation is the task of extracting an object as the part of a particular image. The common drawback of different segmentation methods is that they perform the extraction task incompletely. Tasks like edge detection, denoising and smoothing, as the parts of segmentation, can be done through applying the dictionaries. In this paper, we propose three new contrast stretching function. Based on one of the stretching functions and shearlets as a dictionary, we improved the previous version of a method that has been used in binary segmentation for magnetic resonance angiography images (MRI). We also introduce a three-stage binary image segmentation algorithm for vessel segmentation in MRI images. There are some disadvantages in recent proposed methods when dealing with extracting vessels of medical images. Our algorithm does the task with a more accurate extraction in detecting vessels having low intensity and weak edges in MRI.

Weakly supervised retinal vessel segmentation algorithm without groundtruth

In the current image processing field, medical image segmentation needs a lot of groundtruths, and the process of making these groundtruths is time-consuming and laborious. Thus, a novel retinal vessel segmentation algorithm without groundtruth is proposed in this Letter. The hierarchical clustering algorithm is first used to binary classify vessel and non-vessel pixels. Then classification results based on DRIVE databases are used as pseudo groundtruths to train the neural networks and transfer learning is considered for subsequent processing. Next the trained network is used as the feature extraction tool, by calculating and comparing the difference of image features between the target domain data (DRIVE database) and the source domain data (STARE, CHASE DB1, and HRF databases) extracted from the network. The data required for training is expanded based on semi-supervised clustering in this image feature space, finally the deep neural network is further fine-tuned. Experiments on the publicly available fundus image dataset DRIVE demonstrate that the proposed method outperforms many other state-of-the-art weakly supervised and unsupervised methods.

Construction and verification of retinal vessel segmentation algorithm for color fundus image under BP neural network model

To improve the accuracy of retinal vessel segmentation, a retinal vessel segmentation algorithm for color fundus images based on back-propagation (BP) neural network is proposed according to the characteristics of retinal blood vessels. Four kinds of green channel image enhancement results of adaptive histogram equalization, morphological processing, Gaussian matched filtering, and Hessian matrix filtering are used to form feature vectors. The BP neural network is input to segment blood vessels. Experiments on the color fundus image libraries DRIVE and STARE show that this algorithm can obtain complete retinal blood vessel segmentation as well as connected vessel stems and terminals. When segmenting most small blood vessels, the average accuracy on the DRIVE library reaches 0.9477, and the average accuracy on the STARE library reaches 0.9498, which has a good segmentation effect. Through verification, the algorithm is feasible and effective for blood vessel segmentation of color fundus images and can detect more capillaries.

Unsupervised multiscale retinal blood vessel segmentation using fundus images

Blood vessel segmentation is a vital step in automated diagnosis of retinal diseases. Some retinal diseases progress with structural changes in the vessels whereas in others, vessels may remain unaffected. Segmentation of vessels is inevitable in both the cases. The extracted vessel map can be studied for these structural changes or can be removed to highlight other abnormalities of the retina. This study presents a rule-based retinal blood vessel segmentation algorithm. It implements two multi-scale approaches, local directional-wavelet transform and global curvelet transform, together in a novel manner for vessel enhancement and thereby segmentation. The authors have proposed a generic field-of-view mask for extraction of region-of-interest. Further, a morphological thickness-correction step, to recover vessel-boundary pixels, is also proposed. The significant contribution of this work is, segmentation of fine vessels while preserving the thickness of major vessels. Moreover, the algorithm is robust, as it performs consistently well, on four public databases, DRIVE, STARE, CHASE_DB-1 and HRF. Performance of the proposed algorithm is evaluated in terms of eight measures : accuracy, sensitivity, specificity, precision, F-1 score, G-mean, MCC and AUC, where it has outperformed many other existing methods. Zero data dependency gives the suggested algorithm, an edge over other state-of-the-art supervised methods.

Detection of Blood Vessels in Optic Disc with Maximum Principal Curvature and Wolf Thresholding Algorithms for Vessel Segmentation and Prewitt Edge Detection and Circular Hough Transform for Optic Disc Detection

The retina is the part of the eye that protects parts of light-sensitive cells. The retina consists of four main parts, namely the blood vessel system, fovea, macula and optic discs. Blood vessels are one of the characteristics that can help in the diagnosis of various retinal diseases. This research discusses the application of algorithms for detection of blood vessels in optic discs. The blood vessels are segmented using the Maximum Principal Curvature algorithm. Before the segmentation process, images are filtered using a Gaussian filters and the optic disc removal is performed. After that the segmentation of vessels uses Wolf thresholding to convert images into binary images. The final step in blood vessels segmentation is removing fine lines that are not vessels using morphological operations. Optic disc detection is done using Prewitt edge detection and circular Hough transform. In optic disc detection, the input image is converted to grayscale and then complemented and improved contrast using contrast-limited adaptive histogram equalization. Then, the opening morphology and median filter were performed. After that, the Prewitt edge and circular Hough transform methods are applied to detect the location of the optic disc. After getting the blood vessel segmentation and knowing the location of optic disc, the last stage is combining the results of blood vessel segmentation with the location of the optic disc. The methods applied are quite efficient in detecting blood vessels at the optic location of the disc.

Analysis of Video Retinal Angiography With Deep Learning and Eulerian Magnification

Objective: The aim of this research is to present a novel computer-aided decision support tool in analyzing, quantifying, and evaluating the retinal blood vessel structure from fluorescein angiogram (FA) videos. Methods: The proposed method consists of three phases: i) image registration for large motion removal from FA videos, followed by ii) retinal vessel segmentation, and lastly, iii) segmentation-guided video magnification. In the image registration phase, individual frames of the FA video are spatiotemporally aligned using a novel wavelet-based registration approach to compensate for the global camera and patient motion. In the second phase, a capsule-based neural network architecture is employed to perform the segmentation of retinal vessels for the first time in the literature. In the final phase, a segmentation-guided Eulerian video magnification is proposed for magnifying subtle changes in the retinal video produced by blood flow through the retinal vessels. The magnification is applied only to the segmented vessels, as determined by the capsule network. This minimizes the high levels of noise present in FA videos and maximizes useful information, enabling ophthalmologists to more easily identify potential regions of pathology. Results: The collected FA video dataset consists of 1402 frames from 10 normal subjects (prospective study). Experimental results for retinal vessel segmentation show that the capsule-based algorithm outperforms a state-of-the-art CNN (U-Net), obtaining a higher dice coefficient (85.94%) and sensitivity (92.36%) while using just 5% of the network parameters. Qualitative analysis of the FA videos was performed after the final phase by expert ophthalmologists, supporting the claim that artificial intelligence assisted decision support tool can be helpful for providing a better analysis of blood flow dynamics. Conclusions: The authors introduce a novel computational tool, combining a wavelet-based video registration method with a deep learning capsule-based retinal vessel segmentation algorithm and a Eulerian video magnification technique to quantitatively and qualitatively analyze FA videos. To authors' best knowledge, this is the first-ever development of such a computational tool to assist ophthalmologists with analyzing blood flow in FA videos.

A deep learning oriented method for automated 3D reconstruction of carotid arterial trees from MR imaging.

The scope of this paper is to present a new carotid vessel segmentation algorithm implementing the U-net based convolutional neural network architecture. With carotid atherosclerosis being the major cause of stroke in Europe, new methods that can provide more accurate image segmentation of the carotid arterial tree and plaque tissue can help improve early diagnosis, prevention and treatment of carotid disease. Herein, we present a novel methodology combining the U-net model and morphological active contours in an iterative framework that accurately segments the carotid lumen and outer wall. The method automatically produces a 3D meshed model of the carotid bifurcation and smaller branches, using multispectral MR image series obtained from two clinical centres of the TAXINOMISIS study. As indicated by a validation study, the algorithm succeeds high accuracy (99.1% for lumen area and 92.6% for the perimeter) for lumen segmentation. The proposed algorithm will be used in the TAXINOMISIS study to obtain more accurate 3D vessel models for improved computational fluid dynamics simulations and the development of models of atherosclerotic plaque progression.

Automated computer-assisted detection system for cerebral aneurysms in time-of-flight magnetic resonance angiography using fully convolutional network

BackgroundAs the rupture of cerebral aneurysm may lead to fatal results, early detection of unruptured aneurysms may save lives. At present, the contrast-unenhanced time-of-flight magnetic resonance angiography is one of the most commonly used methods for screening aneurysms. The computer-assisted detection system for cerebral aneurysms can help clinicians improve the accuracy of aneurysm diagnosis. As fully convolutional network could classify the image pixel-wise, its three-dimensional implementation is highly suitable for the classification of the vascular structure. However, because the volume of blood vessels in the image is relatively small, 3D convolutional neural network does not work well for blood vessels.ResultsThe presented study developed a computer-assisted detection system for cerebral aneurysms in the contrast-unenhanced time-of-flight magnetic resonance angiography image. The system first extracts the volume of interest with a fully automatic vessel segmentation algorithm, then uses 3D-UNet-based fully convolutional network to detect the aneurysm areas. A total of 131 magnetic resonance angiography image data are used in this study, among which 76 are training sets, 20 are internal test sets and 35 are external test sets. The presented system obtained 94.4% sensitivity in the fivefold cross-validation of the internal test sets and obtained 82.9% sensitivity with 0.86 false positive/case in the detection of the external test sets.ConclusionsThe proposed computer-assisted detection system can automatically detect the suspected aneurysm areas in contrast-unenhanced time-of-flight magnetic resonance angiography images. It can be used for aneurysm screening in the daily physical examination.

High‐quality retinal vessel segmentation using generative adversarial network with a large receptive field

AbstractRetinal vessel segmentation is of great significance for assisting doctors in diagnosis of ophthalmological diseases such as diabetic retinopathy, macular degeneration and glaucoma. This article proposes a new retinal vessel segmentation algorithm using generative adversarial learning with a large receptive field. A generative network maps an input retinal fundus image to a realistic vessel image while a discriminative network differentiates between images drawn from the database and the generative network. Firstly, the proposed generative network combines shallow features with the upsampled deep features to assemble a more precise vessel image. Secondly, the residual module in the proposed generative and discriminative networks can effectively help deep nets easy to optimize. Moreover, the dilated convolutions in the proposed generative network effectively enlarge the receptive field without increasing the amount of computations. A number of experiments are conducted on two publicly available datasets (DRIVE and STARE) achieving the segmentation accuracy rates of 95.63% and 96.84%, and the average areas under the ROC curve of 98.12% and 98.53%. Performance results show that the proposed automatic retinal vessel segmentation algorithm outperforms state‐of‐the‐art algorithms in many validation metrics. The proposed algorithm can not only detect small tiny blood vessels but also capture large‐scale high‐level semantic vessel features.

Machine-Learning-Based Functional Microcirculation Analysis

Analysis of microcirculation is an important clinical and research task. Functional analysis of the microcirculation allows researchers to understand how blood flowing in a tissues’ smallest vessels affects disease progression, organ function, and overall health. Current methods of manual analysis of microcirculation are tedious and time-consuming, limiting the quick turnover of results. There has been limited research on automating functional analysis of microcirculation. As such, in this paper, we propose a two-step machine-learning-based algorithm to functionally assess microcirculation videos. The first step uses a modified vessel segmentation algorithm to extract the location of vessel-like structures. While the second step uses a 3D-CNN to assess whether the vessel-like structures contained flowing blood. To our knowledge, this is the first application of machine learning for functional analysis of microcirculation. We use real-world labelled microcirculation videos to train and test our algorithm and assess its performance. More precisely, we demonstrate that our two-step algorithm can efficiently analyze real data with high accuracy (90%).

A fractional filter based efficient algorithm for retinal blood vessel segmentation

This paper presents a new fractional filter and an algorithm for retinal blood vessel segmentation. The proposed fractional filter is designed with the help of a weighted fractional derivative and an exponential weight factor. We have utilized the fractional filter and the eigenvalue maps of a local covariance matrix to develop the algorithm for retinal vessel segmentation. The local covariance matrix is formed by a second-order image moment. Experiments are performed on two well-studied evaluation databases named STARE and DRIVE. Experimental results show that the proposed method is computationally efficient, and the average accuracy of the vessel segmentation on STARE and DRIVE databases are 95.73% and 94.76%, respectively. The performance of the proposed method is compared with other existing methods. Simulation results show that the average performance of the proposed method is comparatively better than most of the discussed methods.