Vessel Extraction Research Articles

Multi-nighttime-light data comparison analysis based on image quality values and lit fishing vessel identification effect

Fisheries provide high-quality protein for many people, and their sustainable use is of global concern. Light trapping is a widely used fishing method that takes advantage of the phototropism of fish. Remote sensing technology allows for the monitoring of lit fishing vessels at sea from the air at night, which supports the sustainable management of fisheries. To investigate the potential of different nighttime light remote sensing data for lit fishing vessel identification and applications, we used the fuzzy evaluation method to quantitatively assess images in terms of their radiometric and geometric quality, and Otsu’s method to compare the effects of lit fishing vessel identification. Three kinds of nighttime lighting data from the Defense Meteorological Satellite Program/Operational Linescan System (DMSP/OLS), Visible infrared imaging radiometer suite day/night band (VIIRS/DNB), and Luojia1-01(LJ1-01) were analyzed, compared, and application pointers were constructed. The results are as follows. ①In the image radiation quality evaluation, the information entropy, clarity, and noise performance of the LJ1-01 image are higher than those of the DMSP/OLS and VIIRS/DNB images, where the information entropy value of the LJ1-01 image is nearly 10 times that of VIIRS/DNB and 23 times that of DMSP/OLS. The average gradient value is 14 times that of the image from VIIRS/DNB and 1,600 times that of DMSP/OLS, while its noise is only about 2/3 of the VIIRS/DNB image and 1/3 of the DMSP/OLS image. In the geometric quality assessment, the geometric positioning accuracy and ground sampling accuracy of the VIIRS/DNB image is the best among the three images, with a relative difference percentage of 100.1%, and the LJ1-01 and DMSP/OLS images are relatively lower, at 96.9% and 92.3%, respectively. ② The detection of squid fishing vessels in the Northwest Pacific is taken as an example to compare the identification effects of three types of data: DMSP/OLS, VIIRS/DNB, and LJ1-01. Among these data, DMSP/OLS can effectively identify the position of the lit fishing boat, and VIIRS/DNB images can accurately estimate the spatial position and number of lit fishing boats with large distances. However, in the case of fishing boats gathering or clustering, the number of fishing vessels could not be identified. This led to the detected number of lit fishing vessels being less than the real value. For the VIIRS/DNB and LJ1-01 images with a 5′×8′ span in the same spatiotemporal range using the same batch of pelagic squid fishing vessels, LJ1-01 extracted 18 fishing vessels. VIIRS/DNB extracted 15, indicating that LJ1-01 can distinguish multiple fishing vessels in the lighted overlapping area, thus accurately identifying the number of fishing vessels. The application pointing table generated based on the results of the three data analyses can provide a reference for sensor/image selection for nighttime light remote sensing fishery applications and a basis for more refined fishing vessel identification, extraction, and monitoring.

LEA U-Net: a U-Net-based deep learning framework with local feature enhancement and attention for retinal vessel segmentation

Feature extraction of the retinal blood vessel is one of the crucial tasks in the prediction of ophthalmologic diseases. Important features are extracted based on image segmentation results. The efficiency of vessel segmentation methods could help doctors in the diagnostic of several relevant diseases as early as possible. Recently, U-Net has achieved good results in many medical image segmentation tasks, especially for images of blood vessels. However, due to the limitation of the network structure, some small features could be lost in the transmission process. As a result, there are still many research gaps for U-Net-based retinal vessel segmentation works. In this paper, we propose an improved U-Net based model to segment images of retinal vessels. The improvement focuses on U-Net from two aspects: designing a local feature enhancement module composed of dilated convolution and 1times 1 convolution to enhance the feature extraction of tiny vessels; integrating an attention mechanism with skip connection of the network to highlight features related to vessel segmentation information taken from the down-sampling part to the up-sampling part. The performance of the proposed model was evaluated and compared with several published state-of-the-art approaches on the same public dataset—DRIVE, and the proposed method achieved an accuracy of 0.9563, F1-score of 0.823, TPR of 0.7983, and TNR of 0.9793. The AUC of PRC is 0.9109 and the AUC of ROC is 0.9794. The results proved the potential for clinical applications.

3D vessel extraction using a scale-adaptive hybrid parametric tracker.

3D vessel extraction has great significance in the diagnosis of vascular diseases. However, accurate extraction of vessels from computed tomography angiography (CTA) data is challenging. For one thing, vessels in different body parts have a wide range of scales and large curvatures; for another, the intensity distributions of vessels in different CTA data vary considerably. Besides, surrounding interfering tissue, like bones or veins with similar intensity, also seriously affects vessel extraction. Considering all the above imaging and structural features of vessels, we propose a new scale-adaptive hybrid parametric tracker (SAHPT) to extract arbitrary vessels of different body parts. First, a geometry-intensity parametric model is constructed to calculate the geometry-intensity response. While geometry parameters are calculated to adapt to the variation in scale, intensity parameters can also be estimated to meet non-uniform intensity distributions. Then, a gradient parametric model is proposed to calculate the gradient response based on a multiscale symmetric normalized gradient filter which can effectively separate the target vessel from surrounding interfering tissue. Last, a hybrid parametric model that combines the geometry-intensity and gradient parametric models is constructed to evaluate how well it fits a local image patch. In the extraction process, a multipath spherical sampling strategy is used to solve the problem of anatomical complexity. We have conducted many quantitative experiments using the synthetic and clinical CTA data, asserting its superior performance compared to traditional or deep learning-based baselines.

Simulation of a flexible multi-vessel extraction plant with counter-current contacting between a solid substrate and supercritical CO2

This work develops a program (MVSEPS) to simulate a multi-vessel SuperCritical Fluid Extraction (SCFE) plant operating in a counter-current fashion. MVSEPS was applied to an SCFE to extract hemp seed oil using the shrinking core mass transfer model. A microstructural factor was best-fitted to experimental data from literature, to describe the inner mass transfer of the oil within the substrate, that was used as a parameter in subsequent simulations. The oil concentration in the CO2 stream exiting an extraction vessel, and the productivity, and efficiency of the SCFE plant were studied by changing selected parameters of the process and extraction conditions. The number of extraction vessels operating in series and the cycle time were relevant simulation parameters, because they cannot be studied experimentally at a laboratory scale. Using a larger number of extraction vessels and shorter cycle times increase the plant productivity but decrease the extraction efficiency. Other parameters studied, such as the particle size of the milled substrate, superficial CO2 velocity, extraction temperature and pressure, impact the operation of the SCFE plant similarly to at laboratory scales.

TUnet-LBF: Retinal fundus image fine segmentation model based on transformer Unet network and LBF

Segmentation of retinal fundus images is a crucial part of medical diagnosis. Automatic extraction of blood vessels in low-quality retinal images remains a challenging problem. In this paper, we propose a novel two-stage model combining Transformer Unet (TUnet) and local binary energy function model (LBF), TUnet-LBF, for coarse to fine segmentation of retinal vessels. In the coarse segmentation stage, the global topological information of blood vessels is obtained by TUnet. The neural network outputs the initial contour and the probability maps, which are input to the fine segmentation stage as the priori information. In the fine segmentation stage, an energy modulated LBF model is proposed to obtain the local detail information of blood vessels. The proposed model reaches accuracy (Acc) of 0.9650, 0.9681 and 0.9708 on the public datasets DRIVE, STARE and CHASE_DB1 respectively. The experimental results demonstrate the effectiveness of each component in the proposed model.

LIVE-Net: Comprehensive 3D vessel extraction framework in CT angiography

The extraction of vessels from computed tomography angiography (CTA) is significant in diagnosing and evaluating vascular diseases. However, due to the anatomical complexity, wide intensity distribution, and small volume proportion of vessels, vessel extraction is laborious and time-consuming, and it is easy to lead to error-prone diagnostic results in clinical practice. This study proposes a novel comprehensive vessel extraction framework, called the Local Iterative-based Vessel Extraction Network (LIVE-Net), to achieve 3D vessel segmentation while tracking vessel centerlines. LIVE-Net contains dual dataflow pathways that work alternately: an iterative tracking network and a local segmentation network. The former can generate the fine-grain direction and radius prediction of a vascular patch by using the attention-embedded atrous pyramid network (aAPN), and the latter can achieve 3D vascular lumen segmentation by constructing the multi-order self-attention U-shape network (MOSA-UNet). LIVE-Net is trained and evaluated on two datasets: the MICCAI 2008 Coronary Artery Tracking Challenge (CAT08) dataset and head and neck CTA dataset from the clinic. Experimental results of both tracking and segmentation show that our proposed LIVE-Net exhibits superior performance compared with other state-of-the-art (SOTA) networks. In the CAT08 dataset, the tracked centerlines have an average overlap of 95.2%, overlap until first error of 91.2%, overlap with the clinically relevant vessels of 98.3%, and error distance inside of 0.21 mm. The corresponding tracking overlap metrics in the head and neck CTA dataset are 96.7%, 91.0%, and 99.8%, respectively. In addition, the results of the consistent experiment also show strong clinical correspondence. For the segmentation of bilateral carotid and vertebral arteries, our method can not only achieve better accuracy with an average dice similarity coefficient (DSC) of 90.03%, Intersection over Union (IoU) of 81.97%, and 95% Hausdorff distance (95%HD) of 3.42 mm , but higher efficiency with an average time of 67.25 s , even three times faster compared to some methods applied in full field view. Both the tracking and segmentation results prove the potential clinical utility of our network.

Process optimization and extraction of acids, syringols, guaiacols, phenols and ketones from beech wood slow pyrolysis liquids with supercritical carbon dioxide at different densities

In order to enhance the usability of beech wood slow pyrolysis liquid, an optimization of the extraction process with supercritical carbon dioxide (scCO2) has been carried out. The main goals were to minimize losses and to stabilize the extraction parameters. Different designs of extraction vessels were used to accelerate the extraction and to facilitate the extraction of slow pyrolysis liquids on silica gel as well as in its liquid state. The slow pyrolysis liquid was extracted at different scCO2 densities and temperatures. The densities were approx. 355 and 725 kg/m3 at 313.15 and 333.15 K respectively. A high density is the primary factor in increasing the extraction yield. An elevated temperature at high density level further increased the yields. The distribution of GC-MS detectable compounds of the extracts changed distinctly, resulting in an increase of syringols and a decrease of acids, ketones and furans during extraction.

A learnable Gabor Convolution kernel for vessel segmentation

Vessel segmentation is significant for characterizing vascular diseases, receiving wide attention of researchers. The common vessel segmentation methods are mainly based on convolutional neural networks (CNNs), which have excellent feature learning capabilities. Owing to inability to predict learning direction, CNNs generate large channels or sufficient depth to obtain sufficient features. It may engender redundant parameters. Drawing on performance ability of Gabor filters in vessel enhancement, we built Gabor convolution kernel and designed its optimization. Unlike traditional filter using and common modulation, its parameters are automatically updated using gradients in the back propagation. Since the structural shape of Gabor convolution kernels is the same as that of regular convolution kernels, it can be integrated into any CNNs architecture. We built Gabor ConvNet using Gabor convolution kernels and tested it using three vessel datasets. It scored 85.06%, 70.52% and 67.11%, respectively, ranking first on three datasets. Results shows that our method outperforms advanced models in vessel segmentation. Ablations also proved that Gabor kernel has better vessel extraction ability than the regular convolution kernel.

Segmentation of retinal vessels in fundus images based on U-Net with self-calibrated convolutions and spatial attention modules.

Automated and accurate segmentation of retinal vessels in fundus images is an important step for screening and diagnosing various ophthalmologic diseases. However, many factors, including the variations of vessels in color, shape and size, make this task become an intricate challenge. One kind of the most popular methods for vessel segmentation is U-Net based methods. However, in the U-Net based methods, the size of the convolution kernels is generally fixed. As a result, the receptive field for an individual convolution operation is single, which is not conducive to the segmentation of retinal vessels with various thicknesses. To overcome this problem, in this paper, we employed self-calibrated convolutions to replace the traditional convolutions for the U-Net, which can make the U-Net learn discriminative representations from different receptive fields. Besides, we proposed an improved spatial attention module, instead of using traditional convolutions, to connect the encoding part and decoding part of the U-Net, which can improve the ability of the U-Net to detect thin vessels. The proposed method has been tested on Digital Retinal Images for Vessel Extraction (DRIVE) database and Child Heart and Health Study in England Database (CHASE DB1). The metrics used to evaluate the performance of the proposed method are accuracy (ACC), sensitivity (SE), specificity (SP), F1-score (F1) and the area under the receiver operating characteristic curve (AUC). The ACC, SE, SP, F1 and AUC obtained by the proposed method are 0.9680, 0.8036, 0.9840, 0.8138 and 0.9840 respectively on DRIVE database, and 0.9756, 0.8118, 0.9867, 0.8068 and 0.9888 respectively on CHASE DB1, which are better than those obtained by the traditional U-Net (the ACC, SE, SP, F1 and AUC obtained by U-Net are 0.9646, 0.7895, 0.9814, 0.7963 and 0.9791 respectively on DRIVE database, and 0.9733, 0.7817, 0.9862, 0.7870 and 0.9810 respectively on CHASE DB1). The experimental results indicate that the proposed modifications in the U-Net are effective for vessel segmentation. The structure of the proposed network.

Retinal image blood vessel classification using hybrid deep learning in cataract diseased fundus images

With recent advanced technologies, various automated diagnosis tools were developed to prevent retinal diseases. The automatic segmentation of blood vessels can help detect various retinal diseases and also assist in reducing doctors’ workload. In existing, numerous techniques have been established to segment RBV automatically. But, they failed to provide better accuracy because of higher computational complexity and lower efficiency. Thus, the proposed work introduces an effective hybrid deep learning technique for RBV segmentation and classification of vessels. Initially, the retinal images are pre-processed to enhance the image quality by performing two steps such as image cropping and colour channel conversion. From the pre-processed images, the most important regions are segmented through a new Enhanced Fuzzy C-Means (EFCM) clustering scheme. During the segmentation process, the retinal images are clustered according to the thickness of the blood vessels, which helps minimize the computational complexity. After segmentation, a hybrid deep learning technique like DenseNet and ShuffleNet is introduced to perform feature extraction and classification. The experimental setup is done by the Python platform using the databases like DRIVE (Digital Retinal Images for Vessel Extraction), STARE (STructured Analysis of the Retina), and HRF (High Resolution fundus). Using the DRIVE dataset, the proposed model achieves an accuracy of 99%, the attained accuracy value of the STARE dataset is 98%, and the HRF dataset obtains an accuracy of 98%. The result analysis proves that the proposed hybrid deep learning technique is more efficient than the state-of-the-art techniques.

Design and implementation of diabetic retinopathy detections on retinal images

A retinal picture gives a depiction of what's going on inside the human body. Specifically, the condition of the retinal vessels has been appeared to mirror the cardiovascular state of the body. Retinal pictures give impressive data on neurotic changes brought about by neighbourhood visual illness which uncovers diabetes, hypertension, arteriosclerosis, cardiovascular infection and stroke. PC supported examination of retinal picture assumes a focal part in indicative methodology. Nonetheless, programmed retinal division is muddled by the way that retinal pictures are frequently uproarious, inadequately differentiated, and the vessel widths can shift from enormous to exceptionally little. So in this venture, we can carry out robotize division approach dependent on diagram hypothetical strategy to give local data utilizing measure. We address the portioned vascular construction as a vessel fragment diagram and make the issue of recognize vessels as one of discovering the veins in the chart given a bunch of requirements like CRAE and CRVE. These estimations are found to have great connection with hypertension, coronary illness, and stroke. Be that as it may, they require the precise extraction of particular vessels from a retinal picture. We plan a strategy to tackle this improvement issue and assess it on an enormous genuine world dataset of retinal pictures.

A novel vessel segmentation algorithm for pathological en-face images based on matched filter

The vascular information in fundus images can provide important basis for detection and prediction of retina-related diseases. However, the presence of lesions such as Coroidal Neovascularization can seriously interfere with normal vascular areas in optical coherence tomography (OCT) fundus images. In this paper, a novel method is proposed for detecting blood vessels in pathological OCT fundus images. First of all, an automatic localization and filling method is used in preprocessing step to reduce pathological interference. Afterwards, in terms of vessel extraction, a pore ablation method based on capillary bundle model is applied. The ablation method processes the image after matched filter feature extraction, which can eliminate the interference caused by diseased blood vessels to a great extent. At the end of the proposed method, morphological operations are used to obtain the main vascular features. Experimental results on the dataset show that the proposed method achieves 0.88 ± 0.03, 0.79 ± 0.05, 0.66 ± 0.04, results in DICE, PRECISION and TPR, respectively. Effective extraction of vascular information from OCT fundus images is of great significance for the diagnosis and treatment of retinal related diseases.

Diagnostic performance of deep learning-based vessel extraction and stenosis detection on coronary computed tomography angiography for coronary arterydisease: a multi-reader multi-case study.

Post-processing and interpretation of coronary CT angiography (CCTA) imaging are time-consuming and dependent on the reader's experience. An automated deep learning (DL)-based imaging reconstruction and diagnosis system was developed to improve diagnostic accuracy and efficiency. Our study including 374 cases from five sites, inviting 12 radiologists, assessed the DL-based system in diagnosing obstructive coronary disease with regard to diagnostic performance, imaging post-processing and reporting time of radiologists, with invasive coronary angiography as a standard reference. The diagnostic performance of DL system and DL-assisted human readers was compared with the traditional method of human readers without DL system. Comparing the diagnostic performance of human readers without DL system versus with DL system, the AUC was improved from 0.81 to 0.82 (p < 0.05) at patient level and from 0.79 to 0.81 (p < 0.05) at vessel level. An increase in AUC was observed in inexperienced radiologists (p < 0.05), but was absent in experienced radiologists. Regarding diagnostic efficiency, comparing the DL system versus human reader, the average post-processing and reporting time was decreased from 798.60s to 189.12s (p < 0.05). The sensitivity and specificity of using DL system alone were 93.55% and 59.57% at patient level and 83.23% and 79.97% at vessel level, respectively. With the DL system serving as a concurrent reader, the overall post-processing and reading time was substantially reduced. The diagnostic accuracy of human readers, especially for inexperienced readers, was improved. DL-assisted human reader had the potential of being the reading mode of choice in clinical routine.

Research on Vessel Segmentation and Extraction Method Based on Improved 2D Gabor Features

This study proposed a vessel segmentation method based on Gabor features. According to the eigenvector of Hessian matrix of each pixel in the image, the vessel direction of each point was obtained to set the direction angle of Gabor filter, and the Gabor features of different vessel width at each point were extracted to establish the 6D vectors of each point. By reducing the dimension of the 6D vector, the 2D vector of each point was obtained and fused with the original image G channel. U-Net neural network was used to classify the fused image to segment vessels. The experimental results of this method in DRIVE dataset showed that this method had a good effect on the detection of small vessels and vessels at the intersection.

Deep learning-based end-to-end automated stenosis classification and localization on catheter coronary angiography.

Automatic coronary angiography (CAG) assessment may help in faster screening and diagnosis of stenosis in patients with atherosclerotic disease. We aimed to provide an end-to-end workflow that separates cases with normal or mild stenoses from those with higher stenosis severities to facilitate safety screening of a large volume of the CAG images. A deep learning-based end-to-end workflow was employed as follows: (1) Candidate frame selection from CAG videograms with Convolutional Neural Network (CNN) + Long Short Term Memory (LSTM) network, (2) Stenosis classification with Inception-v3 using 2 or 3 categories (<25%, >25%, and/or total occlusion) with and without redundancy training, and (3) Stenosis localization with two methods of class activation map (CAM) and anchor-based feature pyramid network (FPN). Overall 13,744 frames from 230 studies were used for the stenosis classification training and fourfold cross-validation for image-, artery-, and per-patient-level. For the stenosis localization training and fourfold cross-validation, 690 images with > 25% stenosis were used. Our model achieved an accuracy of 0.85, sensitivity of 0.96, and AUC of 0.86 in per-patient level stenosis classification. Redundancy training was effective to improve classification performance. Stenosis position localization was adequate with better quantitative results in anchor-based FPN model, achieving global-sensitivity for left coronary artery (LCA) and right coronary artery (RCA) of 0.68 and 0.70. We demonstrated a fully automatic end-to-end deep learning-based workflow that eliminates the vessel extraction and segmentation step in coronary artery stenosis classification and localization on CAG images. This tool may be useful to facilitate safety screening in high-volume centers and in clinical trial settings.

Retinal Blood-Vessel Extraction Using Weighted Kernel Fuzzy C-Means Clustering and Dilation-Based Functions.

Automated blood-vessel extraction is essential in diagnosing Diabetic Retinopathy (DR) and other eye-related diseases. However, the traditional methods for extracting blood vessels tend to provide low accuracy when dealing with difficult situations, such as extracting both micro and large blood vessels simultaneously with low-intensity images and blood vessels with DR. This paper proposes a complete preprocessing method to enhance original retinal images before transferring the enhanced images to a novel blood-vessel extraction method by a combined three extraction stages. The first stage focuses on the fast extraction of retinal blood vessels using Weighted Kernel Fuzzy C-Means (WKFCM) Clustering to draw the vessel feature from the retinal background. The second stage focuses on the accuracy of full-size images to achieve regional vessel feature recognition of large and micro blood vessels and to minimize false extraction. This stage implements the mathematical dilation operator from a trained model called Dilation-Based Function (DBF). Finally, an optimal parameter threshold is empirically determined in the third stage to remove non-vessel features in the binary image and improve the overall vessel extraction results. According to evaluations of the method via the datasets DRIVE, STARE, and DiaretDB0, the proposed WKFCM-DBF method achieved sensitivities, specificities, and accuracy performances of 98.12%, 98.20%, and 98.16%, 98.42%, 98.80%, and 98.51%, and 98.89%, 98.10%, and 98.09%, respectively.

Deep learning-based hemodynamic prediction of carotid artery stenosis before and after surgical treatments.

Hemodynamic prediction of carotid artery stenosis (CAS) is of great clinical significance in the diagnosis, prevention, and treatment prognosis of ischemic strokes. While computational fluid dynamics (CFD) is recognized as a useful tool, it shows a crucial issue that the high computational costs are usually required for real-time simulations of complex blood flows. Given the powerful feature-extraction capabilities, the deep learning (DL) methodology has a high potential to implement the mapping of anatomic geometries and CFD-driven flow fields, which enables accomplishing fast and accurate hemodynamic prediction for clinical applications. Based on a brain/neck CT angiography database of 280 subjects, image based three-dimensional CFD models of CAS were constructed through blood vessel extraction, computational domain meshing and setting of the pulsatile flow boundary conditions; a series of CFD simulations were undertaken. A DL strategy was proposed and accomplished in terms of point cloud datasets and a DL network with dual sampling-analysis channels. This enables multimode mapping to construct the image-based geometries of CAS while predicting CFD-based hemodynamics based on training and testing datasets. The CFD simulation was validated with the mass flow rates at two outlets reasonably agreed with the published results. Comprehensive analysis and error evaluation revealed that the DL strategy enables uncovering the association between transient blood flow characteristics and artery cavity geometric information before and after surgical treatments of CAS. Compared with other methods, our DL-based model trained with more clinical data can reduce the computational cost by 7,200 times, while still demonstrating good accuracy (error<12.5%) and flow visualization in predicting the two hemodynamic parameters. In addition, the DL-based predictions were in good agreement with CFD simulations in terms of mean velocity in the stenotic region for both the preoperative and postoperative datasets. This study points to the capability and significance of the DL-based fast and accurate hemodynamic prediction of preoperative and postoperative CAS. For accomplishing real-time monitoring of surgical treatments, further improvements in the prediction accuracy and flexibility may be conducted by utilizing larger datasets with specific real surgical events such as stent intervention, adopting personalized boundary conditions, and optimizing the DL network.

IMPLEMENTATION OF CLAHE CONTRAST ENHANCEMENT &amp; OTSU THRESHOLDING IN RETINAL IMAGE PROCESSING

In both ophthalmological and cardiovascular disease diagnosis, the accurate segmentation of the retinal vessel tree has become the prerequisite step for automatic or computer-aided diagnosis systems. Unlike typical foreground and background segmentation in normal image processing, there are three problems making the retinal vessel segmentation task even harder. First, the retinal color image tends to be red everywhere, thus having a lower contrast than normal image segmentation. Second, most retinal color images suffer from unbalanced illumination and make it harder to recognize background. Third, the symptom for retinopathy has unexpected color and shape, thus making it more difficult to separate vessel from noises. Under such circumstance, the research of the retinal blood vessel segmentation has brought much attention and been developed. In this work, the input is a retinal color image and the output will be a binary image of the vessel and non-vessel pixels. Preprocessing with CLAHE contrast enhancement, Vessel Extraction with Otsu thresholding is implemented along with performance parameters Accuracy, Sensitivity and Specificity. In the proposed work 20 images are used from the database called Digital Retinal Images for Vessel Extraction. The performance parameters Specificity, Sensitivity, Accuracy are evaluated.

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.

Automated extraction of blood vessels in retinal images using Rolling Guidance Filter (RGF)

Automated extraction of blood vessels in retinal images using Rolling Guidance Filter (RGF)