Automatic Vessel Segmentation Research Articles

Retinal blood vessel segmentation using root Guided decision tree assisted enhanced Fuzzy C-mean clustering for disease identification

A number of groundbreaking computer-aided techniques have recently been established for the segmentation of retinal vessels, with clinical applications predominating. Early detection of these abnormalities is critical in preventing individuals from becoming blind. In recent days, automatic vessel segmentation is necessary, but it is a difficult task due to the complex structure and variation in vessel size and shape of retinal vessels. Therefore, in this work, a novel retinal Blood Vessel Segmentation (BVS) has been proposed that utilizes the potential of the Enhanced Fuzzy C-mean Clustering (EFCM) and Root Guided Decision Tree (RGTC). Initially, the input images are pre-processed to enhance the color channel contrast and the Gabor filter is utilized to enhance the small vessels. In the segmentation phase, a 13-dimensional feature vector is constructed, which is then reduced into an optimum feature set by utilizing Principal Component Analysis (PCA). The segmentation phase is utilized to further process the new feature vector. The RGDT is used to undertake reanalysis in non-vessel clustersin order to recover the incorrectly grouped vessel pixels. Finally, the accurate segmented image is obtained through post-processing morphological operations. Simulation has been performed using Python with OpenCV and an accuracy of 97.69% and 97.51% has been achieved using in STARE dataset and DRIVE dataset respectively. Experimental results indicate that the proposed algorithm delivers satisfactory results. As a result, this method is useful as an automated retinal BVS system to assist ophthalmologists.

Multi-Scale Deep Information and Adaptive Attention Mechanism Based Coronary Reconstruction of Superior Mesenteric Artery

Vascular images contain a lot of key information, such as length, diameter and distribution. Thus reconstruction of vessels such as the Superior Mesenteric Artery is critical for the diagnosis of some abdominal diseases. However automatic segmentation of abdominal vessels is extremely challenging due to the multi-scale nature of vessels, boundary-blurring, low contrast, artifact disturbance and vascular cracks in Maximum Intensity Projection images. In this work, we propose a dual attention guided method where an adaptive adjustment field is applied to deal with multi-scale vessel information, and a channel feature fusion module is used to refine the extraction of thin vessels, reducing the interference and background noise. In particular, we propose a novel structure that accepts multiple sequential images as input, and successfully introduces spatial-temporal features by contextual information. A further IterUnet step is introduced to connect tiny cracks caused using CT scans. Comparing our proposed model with other state-of-the-art models, our model yields better segmentation and achieves an average F1 metric of 0.812.

CE-NC-VesselSegNet: Supervised by contrast-enhanced CT images but utilized to segment pulmonary vessels from non-contrast-enhanced CT images

BackgroundThe automatic segmentation of pulmonary vessels from CT images has important significance. However, accurately annotating pulmonary vessels directly in non-contrast CT (NCCT) images is complex and time-consuming. MethodsThis study aims to draw annotations with contrast-enhanced CT (CECT) images and train a deep-learning model for segmenting pulmonary vessels from NCCT images. Two datasets with 63 CT scans were collected. Dataset D1 included 17 cases annotated in CECT images, 10 cases annotated in NCCT images, and 12 NCCT scans. Dataset D2 consisted of 12 CECT and 12 NCCT scans with annotations. First, annotations drawn in CECT images (Dataset D1) are transferred to NCCT images via spatial registration. Second, a CE-NC-VesselSegNet is proposed and trained using the transferred annotations to segment pulmonary vessels from NCCT images. Finally, the CE-NC-VesselSegNet is evaluated and compared with its counterparts. ResultsAfter registration, the maximum and root mean square error between CECT and NCCT images decreases, while the structural similarity and peak signal-to-noise ratio increase. CE-NC-VesselSegNet can accurately segment pulmonary vessels from NCCT images with a Dice of 0.856. In the external validation using Dataset D2, the CE-NC-VesselSegNet achieves a Dice of 0.738, which is higher compared with that of NC-VesselSegNet trained by D2. Visual inspections have shown that CE-NC-VesselSegNet enables more accurate and continuous segmentation compared with its counterpart. ConclusionsAnnotations of pulmonary vessels drawn in CECT images can be transferred to NCCT images via spatial registration. Using these transferred annotations of high quality, a CE-NC-VesselSegNet can be trained to segment pulmonary vessels from NCCT images.

Wave-Net: A lightweight deep network for retinal vessel segmentation from fundus images

Accurate segmentation of retinal vessels from fundus images is fundamental for the diagnosis of numerous diseases of eye, and an automated vessel segmentation method can effectively help clinicians to make accurate diagnosis for the patients and provide the appropriate treatment schemes. It is important to note that both thick and thin vessels play the key role for disease judgements. Because of complex factors, the precise segmentation of thin vessels is still a great challenge, such as the presence of various lesions, image noise, complex backgrounds and poor contrast in the fundus images. Recently, because of the advantage of context feature representation learning capabilities, deep learning has shown a remarkable segmentation performance on retinal vessels. However, it still has some shortcomings on high-precision retinal vessel extraction due to some factors, such as semantic information loss caused by pooling operations, limited receptive field, etc. To address these problems, this paper proposes a new lightweight segmentation network for precise retinal vessel segmentation, which is called as Wave-Net model on account of the whole shape. To alleviate the influence of semantic information loss problem to thin vessels, to acquire more contexts about micro structures and details, a detail enhancement and denoising block (DED) is proposed to improve the segmentation precision on thin vessels, which replaces the simple skip connections of original U-Net. On the other hand, it could well alleviate the influence of the semantic gap problem. Further, faced with limited receptive field, for multi-scale vessel detection, a multi-scale feature fusion block (MFF) is proposed to fuse cross-scale contexts to achieve higher segmentation accuracy and realize effective processing of local feature maps. Experiments indicate that proposed Wave-Net achieves an excellent performance on retinal vessel segmentation while maintaining a lightweight network design compared to other advanced segmentation methods, and it also has shown a better segmentation ability to thin vessels.

Local-Sensitive Connectivity Filter (LS-CF): A Post-Processing Unsupervised Improvement of the Frangi, Hessian and Vesselness Filters for Multimodal Vessel Segmentation.

A retinal vessel analysis is a procedure that can be used as an assessment of risks to the eye. This work proposes an unsupervised multimodal approach that improves the response of the Frangi filter, enabling automatic vessel segmentation. We propose a filter that computes pixel-level vessel continuity while introducing a local tolerance heuristic to fill in vessel discontinuities produced by the Frangi response. This proposal, called the local-sensitive connectivity filter (LS-CF), is compared against a naive connectivity filter to the baseline thresholded Frangi filter response and to the naive connectivity filter response in combination with the morphological closing and to the current approaches in the literature. The proposal was able to achieve competitive results in a variety of multimodal datasets. It was robust enough to outperform all the state-of-the-art approaches in the literature for the OSIRIX angiographic dataset in terms of accuracy and 4 out of 5 works in the case of the IOSTAR dataset while also outperforming several works in the case of the DRIVE and STARE datasets and 6 out of 10 in the CHASE-DB dataset. For the CHASE-DB, it also outperformed all the state-of-the-art unsupervised methods.

Abdominal vessel segmentation using vessel model embedded fuzzy C-means and similarity from CT angiography.

The accurate abdominal vessel segmentation of CT angiography (CTA) data is essential for diagnosis and surgical planning. However, accurate abdominal vessel segmentation is a difficult problem since the following challenges: (1) complex abdominal vessel structure containing a wide range size of vessel branches, (2) low contrast of small vessels, and (3) uneven distribution of vessel grayscale. With full consideration of the challenges, we propose an automatic vessel segmentation algorithm. For challenge 1, the algorithm's framework is divided into large and small vessel segmentation and has the following steps. Firstly, a vessel model embedded fuzzy c-means (VMEFCM) method with full consideration of challenge 2 is presented to obtain the initial vessel voxels. Then, considering challenge 3, a large vessel segmentation method based on the initial vessel voxels, similarity, and morphologic is proposed. Finally, a small vessel segmentation method based on spine is described. Extensive analysis is carried out on simulation datasets and 78 CTA datasets. The experimental results indicate that each step of the algorithm achieves the prospective results, and the proposed algorithm is effective and accurate with low computational cost. The dice, sensitivity, Jaccard coefficient, and precision rate were 93.7±2.8%, 93.7±2.8%, 88.2±4.8%, and 94.2±7.5% respectively.

Retinal Vessel Segmentation, a Review of Classic and Deep Methods.

Retinal illnesses such as diabetic retinopathy (DR) are the main causes of vision loss. In the early recognition of eye diseases, the segmentation of blood vessels in retina images plays an important role. Different symptoms of ocular diseases can be identified by the geometric features of ocular arteries. However, due to the complex construction of the blood vessels and their different thicknesses, segmenting the retina image is a challenging task. There are a number of algorithms that helped the detection of retinal diseases. This paper presents an overview of papers from 2016 to 2022 that discuss machine learning and deep learning methods for automatic vessel segmentation. The methods are divided into two groups: Deep learning-based, and classic methods. Algorithms, classifiers, pre-processing and specific techniques of each group is described, comprehensively. The performances of recent works are compared based on their achieved accuracy in different datasets in inclusive tables. A survey of most popular datasets like DRIVE, STARE, HRF and CHASE_DB1 is also given in this paper. Finally, a list of findings from this review is presented in the conclusion section.

DSA Image Analysis of Clinical Features and Nursing Care of Cerebral Aneurysm Patients Based on the Deep Learning Algorithm.

Objective A deep learning algorithm was developed for automatic detection and localization of intracranial aneurysms in DSA, and its clinical characteristics were analyzed, and targeted nursing measures were formulated. Methods Using a retrospective multicenter study method based on radiology reports, DSA images of aneurysms were randomly divided into 75 cases in the training set, 20 cases in the internal test set, and 35 cases in the external test set. Using a computer-aided detection method based on the three-dimensional U-Net (3D U-Net), after preprocessing DSA images, automatic segmentation of intracranial blood vessels is performed to obtain regions of interest, and based on the segmentation results, physicians' annotations are introduced. The 3D U-Net network model is trained and adjusted, and the obtained model is used to automatically detect the cerebral aneurysm area. Results Fivefold cross-validation was used for the training set and the internal test set, and a sensitivity of (94.4 ± 1.1)% was obtained. Automatic detection of aneurysms was performed on the external test set, and the average false positive rate was 0.86 FPs/case (false positives/case). The resulting sensitivity was 82.9%. The classification comparison of external test sets showed that the sensitivity of the method for detecting aneurysms with sizes of 5.00~<10.00 mm and ≥10.00 mm (88.2% and 100.0%) was higher than that for aneurysms with sizes of <3.00 mm and 3.00~<5.00 mm (50.0% and 72.7%). The sensitivity of patients aged 50-60 years and >60 years (90.0% and 87.5%) was higher than that of patients aged <50 years (66.7%), and there was little difference between different genders (84.6% in males and 81.8% in females). Conclusion The deep learning algorithm has high diagnostic performance in detecting intracranial aneurysms, which is verified by external datasets.

Unsupervised domain adaptation method for segmenting cross-sectional CCA images

Background and Objectives: Automatic vessel segmentation in ultrasound is challenging due to the quality of the ultrasound images, which is affected by attenuation, high level of speckle noise and acoustic shadowing. Recently, deep convolutional neural networks are increasing in popularity due to their great performance on image segmentation problems, including vessel segmentation. Traditionally, large labeled datasets are required to train a network that achieves high performance, and is able to generalize well to different orientations, transducers and ultrasound scanners. However, these large datasets are rare, given that it is challenging and time-consuming to acquire and manually annotate in-vivo data.Methods: In this work, we present a model-based, unsupervised domain adaptation method that consists of two stages. In the first stage, the network is trained on simulated ultrasound images, which have an accurate ground truth. In the second stage, the network continues training on in-vivo data in an unsupervised way, therefore not requiring the data to be labelled. Rather than using an adversarial neural network, prior knowledge on the elliptical shape of the segmentation mask is used to detect unexpected outputs.Results: The segmentation performance was quantified using manually segmented images as ground truth. Due to the proposed domain adaptation method, the median Dice similarity coefficient increased from 0 to 0.951, outperforming a domain adversarial neural network (median Dice 0.922) and a state-of-the-art Star-Kalman algorithm that was specifically designed for this dataset (median Dice 0.942).Conclusions: The results show that it is feasible to first train a neural network on simulated data, and then apply model-based domain adaptation to further improve segmentation performance by training on unlabeled in-vivo data. This overcomes the limitation of conventional deep learning approaches to require large amounts of manually labeled in-vivo data. Since the proposed domain adaptation method only requires prior knowledge on the shape of the segmentation mask, performance can be explored in various domains and applications in future research.

The RETA Benchmark for Retinal Vascular Tree Analysis

Topological and geometrical analysis of retinal blood vessels could be a cost-effective way to detect various common diseases. Automated vessel segmentation and vascular tree analysis models require powerful generalization capability in clinical applications. In this work, we constructed a novel benchmark RETA with 81 labelled vessel masks aiming to facilitate retinal vessel analysis. A semi-automated coarse-to-fine workflow was proposed for vessel annotation task. During database construction, we strived to control inter-annotator and intra-annotator variability by means of multi-stage annotation and label disambiguation on self-developed dedicated software. In addition to binary vessel masks, we obtained other types of annotations including artery/vein masks, vascular skeletons, bifurcations, trees and abnormalities. Subjective and objective quality validations of the annotated vessel masks demonstrated significantly improved quality over the existing open datasets. Our annotation software is also made publicly available serving the purpose of pixel-level vessel visualization. Researchers could develop vessel segmentation algorithms and evaluate segmentation performance using RETA. Moreover, it might promote the study of cross-modality tubular structure segmentation and analysis.

Development of an approach to extracting coronary arteries and detecting stenosis in invasive coronary angiograms.

Purpose: In stable coronary artery disease (CAD), reduction in mortality and/or myocardial infarction with revascularization over medical therapy has not been reliably achieved. Coronary arteries are usually extracted to perform stenosis detection. As such, developing accurate segmentation of vascular structures and quantification of coronary arterial stenosis in invasive coronary angiograms (ICA) is necessary. Approach: A multi-input and multiscale (MIMS) U-Net with a two-stage recurrent training strategy was proposed for the automatic vessel segmentation. The proposed model generated a refined prediction map with the following two training stages: (i)stage I coarsely segmented the major coronary arteries from preprocessed single-channel ICAs and generated the probability map of arteries; and (ii)during the stage II, a three-channel image consisting of the original preprocessed image, a generated probability map, and an edge-enhanced image generated from the preprocessed image was fed to the proposed MIMS U-Net to produce the final segmentation result. After segmentation, an arterial stenosis detection algorithm was developed to extract vascular centerlines and calculate arterial diameters to evaluate stenotic level. Results: Experimental results demonstrated that the proposed method achieved an average Dice similarity coefficient of 0.8329, an average sensitivity of 0.8281, and an average specificity of 0.9979 in our dataset with 294 ICAs obtained from 73 patients. Moreover, our stenosis detection algorithm achieved a true positive rate of 0.6668 and a positive predictive value of 0.7043. Conclusions: Our proposed approach has great promise for clinical use and could help physicians improve diagnosis and therapeutic decisions for CAD.

Analysis of Vessel Segmentation Based on Various Enhancement Techniques for Improvement of Vessel Intensity Profile.

It is vital to develop an appropriate prediction model and link carefully to measurable events such as clinical parameters and patient outcomes to analyze the severity of the disease. Timely identifying retinal diseases is becoming more vital to prevent blindness among young and adults. Investigation of blood vessels delivers preliminary information on the existence and treatment of glaucoma, retinopathy, and so on. During the analysis of diabetic retinopathy, one of the essential steps is to extract the retinal blood vessel accurately. This study presents an improved Gabor filter through various enhancement approaches. The degraded images with the enhancement of certain features can simplify image interpretation both for a human observer and for machine recognition. Thus, in this work, few enhancement approaches such as Gamma corrected adaptively with distributed weight (GCADW), joint equalization of histogram (JEH), homomorphic filter, unsharp masking filter, adaptive unsharp masking filter, and particle swarm optimization (PSO) based unsharp masking filter are taken into consideration. In this paper, an effort has been made to improve the performance of the Gabor filter by combining it with different enhancement methods and to enhance the detection of blood vessels. The performance of all the suggested approaches is assessed on publicly available databases such as DRIVE and CHASE_DB1. The results of all the integrated enhanced techniques are analyzed, discussed, and compared. The best result is delivered by PSO unsharp masking filter combined with the Gabor filter with an accuracy of 0.9593 for the DRIVE database and 0.9685 for the CHASE_DB1 database. The results illustrate the robustness of the recommended model in automatic blood vessel segmentation that makes it possible to be a clinical support decision tool in diabetic retinopathy diagnosis.

Age-Related Tortuosity of Carotid and Vertebral Arteries: Quantitative Evaluation With MR Angiography.

Background and PurposeThe vascular tortuosity (VT) of the internal carotid artery (ICA), and vertebral artery (VA) can impact blood flow and neuronal function. However, few studies involved quantitative investigation of VT based on magnetic resonance imaging (MRI). The main purpose of our study was to evaluate the age and gender effects on ICA and VA regarding the tortuosity and flow changes by applying automatic vessel segmentation, centerline tracking, and phase mapping on MR angiography.MethodsA total of 247 subjects (86 males and 161 females) without neurological diseases participated in this study. All subjects obtained T1-weighted MRI, 3D time-of-flight MR angiography, and 2D phase-contrast (PC) MRI scans. To generate quantitative tortuosity metrics from TOF images, the vessel segmentation and centerline tracking were implemented based on Otsu thresholding and fast marching algorithms, respectively. Blood flow and velocity were measured using PC MRI. Among the 247 subjects, 144 subjects (≤ 60 years, 49 males/95 females) were categorized as the young group; 103 subjects (>60 years, 37 males/66 females) were categorized as the old group.ResultsIndependent t-test showed that older subjects had higher tortuosity metrics, whereas lower blood flow and velocity than young subjects (p < 0.0025, Bonferroni-corrected). Cerebral blood flow calculated using the sum flux of four target arteries normalized by the brain mass also showed significantly lower values in older subjects (p < 0.001). The age was observed to be positively correlated with the VT metrics. Compared to the males, the females demonstrated higher geometric indices within VAs as well as faster age-related vascular profile changes. After adjusting age and gender as covariates, maximum blood velocity is negatively correlated with geometric measurements. No association was observed between blood flux and geometric measures.ConclusionsVascular auto-segmentation, centerline tracking, and phase mapping provide promising quantitative assessments of tortuosity and its effects on blood flow. The neck arteries demonstrate quantifiable and significant age-related morphological and hemodynamic alterations. Moreover, females showed more distinct vascular changes with age. Our work is built upon a comprehensive quantitative investigation of a large cohort of populations covering adult lifespan using MRI, the results can serve as reference ranges of each decade in the general population.

A Metaphorical Interpretation on Vessel Segmentation Algorithms by using Local Connected Fractal Dimension

ABSTRACT Retinal vessel segmentation is a prime utensil in ophthalmology. From the last two decades, a massive number of studies have been carried out in the creation and development of automated vessel segmentation. A benefit of this procedure has been recognised in various retinal oddities. A keen mathematical tool that anticipates the degree of difficulty of disease is the fractal dimension. Typically, the fractal dimension depends on the complexity of the objects. In the case of vessel segmentation, we placed filters and contrast adjustments for obtaining a better configuration of the original images. As well as, precise visualisation, identification, prevention medication and surgery planning for particular diseases can be done in an effective manner by considering this segmentation procedure. This will enhance the feature of the skeletonized/segmented structure of the retinal vessels. Hence, focusing on the segmentation method is more important. As a consequence, determining an elite strategy for vessel segmentation remains a challenging task. Thus, this article mainly focuses on the segmentation procedures in four ways and it uses the local connected fractal dimension (for short, ). Also, a precise method for segmenting the source images has been fixed in such a way that manipulating the fractal dimension of the output images occurred in those four processes. Ultimately, it results in the best technique by focusing on the variation of the fractal dimension of the raw pictures, which yields that the U-Net segmentation is more effective.

Self-attentional microvessel segmentation via squeeze-excitation transformer Unet

Automatic vessel segmentation is a key step of clinical or pre-clinical vessel bio-markers for clinical diagnosis. In previous research, the segmentation architectures are mainly based on Convolutional Neural Networks (CNN). However, due to the limitation of the receipt of field (ROF) of convolution operation, it is difficult to further improve the accuracy of the CNN-based methods. To solve this problem, a Squeeze-Excitation Transformer U-net (SETUnet) is proposed to break the ROF limitation of CNN. The proposed squeeze-excitation Transformer can introduce the self attention mechanism into the vessel segmentation task by generating a global attention mapping according to the entire vessel image. To test the performance of the proposed SETUnet, the SETUnet is trained and tested on several public vessel data-sets. The results show that the SETUnet outperforms several state-of-the-art vessel segmentation neural networks, especially on the connectivity of the segmented vessels.

A Deep Learning System for Fully Automated Retinal Vessel Measurement in High Throughput Image Analysis.

MotivationRetinal microvasculature is a unique window for predicting and monitoring major cardiovascular diseases, but high throughput tools based on deep learning for in-detail retinal vessel analysis are lacking. As such, we aim to develop and validate an artificial intelligence system (Retina-based Microvascular Health Assessment System, RMHAS) for fully automated vessel segmentation and quantification of the retinal microvasculature.ResultsRMHAS achieved good segmentation accuracy across datasets with diverse eye conditions and image resolutions, having AUCs of 0.91, 0.88, 0.95, 0.93, 0.97, 0.95, 0.94 for artery segmentation and 0.92, 0.90, 0.96, 0.95, 0.97, 0.95, 0.96 for vein segmentation on the AV-WIDE, AVRDB, HRF, IOSTAR, LES-AV, RITE, and our internal datasets. Agreement and repeatability analysis supported the robustness of the algorithm. For vessel analysis in quantity, less than 2 s were needed to complete all required analysis.

Efficient blood vessel segmentation from color fundus image using deep neural network

Blood vessel segmentation is an essential element of automatic retinal disease screening systems. In particular, retinal blood vessel analysis from fundus image is vital in the identification and diagnosis of cardiovascular and ophthalmological diseases (Ex: Diabetic Retinopathy, Macular degeneration, Retinal Pigmentosa, Macular Edema, and various stages of Glaucoma, etc). Wherefore, the diagnosis of these diseases by automatic vessel segmentation has become essential, especially in disclosure of premature prognosis of vision condition. In general, blood vessel extraction is divided into vessel tracking and pixel classification. In vessel tracking a vasculature model is expanded from a seed point. In pixel classification, the classifier classifies the pixels as either a vessel or background pixel, which is demonstrated in the proposed architecture. In this paper, deep learning based 19 layer U-Net architecture is proposed for the accurate and efficient segmentation of blood vessels. Prior to segmentation, a pre-processing block of AlexNet architecture is introduced for the classification of high-quality images from the experimented databases. This pre-classification stage helps in efficiently picking high-quality images determined by clarity, field definition, and sharpness. AlexNet classification is pivotal in enhancing the overall performance of the system by segmenting fine and tiny blood vessels. The proposed U-Net architecture has an encoder-decoder framework with 9 and 5 convolutional layers in each respectively. In order to boost the efficiency of the network as well as to reduce training and testing time, a proper choice of kernel dimension and number of filters are necessary. Our architecture was investigated on popular databases such as DRIVE, ARIA_d and MESSIDOR and various performance measures (accuracy, sensitivity, specificity, sensibility, Dice coefficient, and Jaccard coefficient) have been computed along with the Receiver Operating Characteristics. It is observed that the accuracy for DRIVE, ARIA_d and MESSIDOR are 90.60%, 87.60% and 83.42%, respectively. Area under curve in Receiver Operating Characteristics plot is found to be 98.54%, 93.28% and 88.18%, for DRIVE, ARIA_d and MESSIDOR databases, respectively. Results with the proposed architecture show remarkable improvement in the performance metrics for blood vessel segmentation.

Multi-Level Attention Network for Retinal Vessel Segmentation.

Automatic vessel segmentation in the fundus images plays an important role in the screening, diagnosis, treatment, and evaluation of various cardiovascular and ophthalmologic diseases. However, due to the limited well-annotated data, varying size of vessels, and intricate vessel structures, retinal vessel segmentation has become a long-standing challenge. In this paper, a novel deep learning model called AACA-MLA-D-UNet is proposed to fully utilize the low-level detailed information and the complementary information encoded in different layers to accurately distinguish the vessels from the background with low model complexity. The architecture of the proposed model is based on U-Net, and the dropout dense block is proposed to preserve maximum vessel information between convolution layers and mitigate the over-fitting problem. The adaptive atrous channel attention module is embedded in the contracting path to sort the importance of each feature channel automatically. After that, the multi-level attention module is proposed to integrate the multi-level features extracted from the expanding path, and use them to refine the features at each individual layer via attention mechanism. The proposed method has been validated on the three publicly available databases, i.e. the DRIVE, STARE, and CHASE _ DB1. The experimental results demonstrate that the proposed method can achieve better or comparable performance on retinal vessel segmentation with lower model complexity. Furthermore, the proposed method can also deal with some challenging cases and has strong generalization ability.

Robust deep 3-D architectures based on vascular patterns for liver vessel segmentation

Deep Learning is providing new solutions for medical image segmentation problems and becomes a key point for future clinical application. The study of vascular structures is a challenging task due to the extremely small size of the vessel structure, low SNR, and varying contrast in medical image data.In this study, we present an end-to-end deep learning segmentation method relying on the integration of vessel enhancement filters inside a 3-D U-Net based architecture. In particular, the raw data used in the learning process -or used as input- is preprocessed using these filters. The use of vesselness filters can significantly improve the contrast in raw images; this step can also help to improve segmentation decision especially on bifurcations. 3-D U-Net, Dense U-Net and MultiRes U-Net are pitted against each other in the vessel segmentation task with the public IRCAD dataset. Considering the integration of vesselness filters, the model parameters were optimized in order to identify the optimal configuration for fully automatic segmentation of hepatic vessels. In addition the three architectures were tested on full 3-D images and slabs (stacks of 2-D slices). The results showed that the most accurate setup is the full 3-D process, which provides the highest Dice for most of the considered models. The 3-D Dense U-Net also gives the best results compared to other models with or without vessel enhancement filters.

Learning based multi-scale feature fusion for retinal blood vessels segmentation

Many eye-related diseases will lead to blindness or worse when it is lack of treatment in the early stages of the disease. Retinal vessel is important for doctors to detect eye diseases, even though the increase of some thin vessels may also mean the occurrence of certain diseases. Therefore, automatic retinal vessel segmentation is of great help to doctors in diagnosing diseases. In this paper, an automatic vessel segmentation method is proposed for retinal image, which is based on support vector machine combining multi-scale feature fusion model and B-COSFIRE filter response. Firstly, the inverted green channel image is enhanced by B-COSFIRE filter to strengthen bar-like vessel structures. Then the features are extracted by means of line operator in a multiresolution way, namely that each filtered image is down-sampled to cover a wider area, hence each sampled pixels can obtain not only the global but also local information. Then the final obtained features from three scales together along the depth direction are combined to train the SVM model. Finally, we use the classifier model to predict blood vessels. The proposed algorithm is evaluated on the public available fundus images datasets (DRIVE: Precision = 0.8657, Se = 0.7088, Sp = 0.9660 and ACC = 0.9900; STARE: Precision = 0.8782, Se = 0.6189, Sp = 0.9908 and ACC = 0.9494). The experiment results show that our proposed algorithm has effects on retinal vessels segmentation.