Small Vessel Segments Research Articles

Experience with a novel high frequency optical coherence tomography device for intracoronary imaging: a case series.

Intravascular imaging, especially optical coherence tomography (OCT), has significantly improved percutaneous coronary intervention (PCI), yet its routine clinical application faces challenges. This case series introduces the Gentuity® High-Frequency Optical Coherence Tomography (HF-OCT), a novel device designed to enhance intracoronary imaging with a significantly faster pullback and smaller catheter size, potentially offering enhanced navigability in complex lesions. We aimed to assess the image quality of Gentuity® HF-OCT in complex vessel conditions, as well as presenting a case series to illustrate the application of the device in various clinical scenarios. In this case series, we included all patients who underwent intracoronary HF-OCT imaging at our center. The primary endpoint was image quality assessed by clear image length (CIL). Image quality was assessed in relation to (1) lesion severity assessed by minimum lumen area (MLA); (2) vessel size, differentiating between larger (diameter ≥ 4 mm) and smaller vessel segments; (3) pre- vs. post-PCI conditions, and (4) vessel tortuosity, categorized into none, moderate, and severe. Twenty-four HF-OCT runs from 14 patients were included. No significant differences in CIL were observed across lesion severity terciles (p = 0.449), between small and large vessel segments [mean CIL% difference 1.3%; confidence interval (CI), -9.3 to 11.8; p = 0.802], and pre- vs. post-PCI conditions (mean CIL difference -3.9 mm; CI, -14.0 to 6.1; p = 0.373). Vessel tortuosity significantly impacted image quality, with clear reductions in CIL observed in cases of moderate (74.8; CI, 73.5 to 76.0; vs. 63.9; CI, 56.2 to 71.5; p = 0.043) and severe tortuosity (74.8; CI, 73.5 to 76.0; vs. 65.0; CI, 62.1 to 67.9; p = 0.002) compared to vessels with no tortuosity. Overall, the HF-OCT demonstrated excellent catheter deliverability and crossability, with very satisfactory image quality and no significant adverse events. The Gentuity® HF-OCT is a new OCT device capable of navigating both small- and large-diameter vessels, with similar image quality, but vessel tortuosity seems to have an impact on image quality. It appears to be as usable as conventional OCT for pre-PCI diagnosis and OCT-guided PCI, potentially bringing additional benefits in terms of deliverability, lesion crossover and ease of use in routine clinical practice.

Improved fully convolutional neuron networks on small retinal vessel segmentation using local phase as attention.

Retinal images have been proven significant in diagnosing multiple diseases such as diabetes, glaucoma, and hypertension. Retinal vessel segmentation is crucial for the quantitative analysis of retinal images. However, current methods mainly concentrate on the segmentation performance of overall retinal vessel structures. The small vessels do not receive enough attention due to their small percentage in the full retinal images. Small retinal vessels are much more sensitive to the blood circulation system and have great significance in the early diagnosis and warning of various diseases. This paper combined two unsupervised methods, local phase congruency (LPC) and orientation scores (OS), with a deep learning network based on the U-Net as attention. And we proposed the U-Net using local phase congruency and orientation scores (UN-LPCOS), which showed a remarkable ability to identify and segment small retinal vessels. A new metric called sensitivity on a small ship (Sesv ) was also proposed to evaluate the methods' performance on the small vessel segmentation. Our strategy was validated on both the DRIVE dataset and the data from Maastricht Study and achieved outstanding segmentation performance on both the overall vessel structure and small vessels.

An attention residual u-net with differential preprocessing and geometric postprocessing: Learning how to segment vasculature including intracranial aneurysms.

Intracranial aneurysms (IA) are lethal, with high morbidity and mortality rates. Reliable, rapid, and accurate segmentation of IAs and their adjacent vasculature from medical imaging data is important to improve the clinical management of patients with IAs. However, due to the blurred boundaries and complex structure of IAs and overlapping with brain tissue or other cerebral arteries, image segmentation of IAs remains challenging. This study aimed to develop an attention residual U-Net (ARU-Net) architecture with differential preprocessing and geometric postprocessing for automatic segmentation of IAs and their adjacent arteries in conjunction with 3D rotational angiography (3DRA) images. The proposed ARU-Net followed the classic U-Net framework with the following key enhancements. First, we preprocessed the 3DRA images based on boundary enhancement to capture more contour information and enhance the presence of small vessels. Second, we introduced the long skip connections of the attention gate at each layer of the fully convolutional decoder-encoder structure to emphasize the field of view (FOV) for IAs. Third, residual-based short skip connections were also embedded in each layer to implement in-depth supervision to help the network converge. Fourth, we devised a multiscale supervision strategy for independent prediction at different levels of the decoding path, integrating multiscale semantic information to facilitate the segmentation of small vessels. Fifth, the 3D conditional random field (3DCRF) and 3D connected component optimization (3DCCO) were exploited as postprocessing to optimize the segmentation results. Comprehensive experimental assessments validated the effectiveness of our ARU-Net. The proposed ARU-Net model achieved comparable or superior performance to the state-of-the-art methods through quantitative and qualitative evaluations. Notably, we found that ARU-Net improved the identification of arteries connecting to an IA, including small arteries that were hard to recognize by other methods. Consequently, IA geometries segmented by the proposed ARU-Net model yielded superior performance during subsequent computational hemodynamic studies (also known as "patient-specific" computational fluid dynamics [CFD] simulations). Furthermore, in an ablation study, the five key enhancements mentioned above were confirmed. The proposed ARU-Net model can automatically segment the IAs in 3DRA images with relatively high accuracy and potentially has significant value for clinical computational hemodynamic analysis.

A retinal vessel segmentation method based improved U-Net model

There are two problems in retinal blood vessel segmentation, which are the insufficient segmentation of small vessels due to the complex curvature morphology of blood vessels and the segmentation difficulty of blood vessels due to uneven brightness background of lesion fundus images. To solve the problems, a series deformable convolution structure is proposed in this paper, which could improve theadaptive features extraction ability to the blood vessels with various shapes and sizes, enhance feature transmissions and alleviate exploding gradients. On this basis, a retinal vessel segmentation method with series deformable convolution and attention mechanism based on U-Net structure (SDAU-Net) is proposed. In SDAU-Net, the convolution module in U-Net is replaced by series deformable convolution module, the lightweight attention module and dual attention module are applied in the decoder part, which effectively improve the U-Net feature extraction ability for the small vessels with complex morphology and the retinopathy images. To verify the SDAU-Net effect, the comparative experiments are conducted on datasets of DRIVE, STARE, CHASE_DB1 and IOSTAR. The results show that SDAU-Net is superior to comparative methods in accuracy. The Se and Acc on the DRIVE and STARE are 0.8293, 0.9675, 0.8973 and 0.9833 respectively, which indicate that SDAU-Net has more advantages in small vessel segmentation and lesion images. To verify the generalization and extendibility, cross-dataset and cross-modality experiments are conducted on DRIVE, STARE and IOSTAR, the results demonstrate outstanding generalization and extendibility of SDAU-Net.

Shape-Aware 3D Small Vessel Segmentation with Local Contrast Guided Attention.

The automated segmentation and analysis of small vessels from in vivo imaging data is an important task for many clinical applications. While current filtering and learning methods have achieved good performance on the segmentation of large vessels, they are sub-optimal for small vessel detection due to their apparent geometric irregularity and weak contrast given the relatively limited resolution of existing imaging techniques. In addition, for supervised learning approaches, the acquisition of accurate pixel-wise annotations in these small vascular regions heavily relies on skilled experts. In this work, we propose a novel self-supervised network to tackle these challenges and improve the detection of small vessels from 3D imaging data. First, our network maximizes a novel shape-aware flux-based measure to enhance the estimation of small vasculature with non-circular and irregular appearances. Then, we develop novel local contrast guided attention(LCA) and enhancement(LCE) modules to boost the vesselness responses of vascular regions of low contrast. In our experiments, we compare with four filtering-based methods and a state-of-the-art self-supervised deep learning method in multiple 3D datasets to demonstrate that our method achieves significant improvement in all datasets. Further analysis and ablation studies have also been performed to assess the contributions of various modules to the improved performance in 3D small vessel segmentation. Our code is available at https://github.com/dengchihwei/LCNetVesselSeg.

MIC-Net: multi-scale integrated context network for automatic retinal vessel segmentation in fundus image.

Accurate retinal vessel segmentation is of great value in the auxiliary screening of various diseases. However, due to the low contrast between the ends of the branches of the fundus blood vessels and the background, and the variable morphology of the optic disc and cup in the retinal image, the task of high-precision retinal blood vessel segmentation still faces difficulties. This paper proposes a multi-scale integrated context network, MIC-Net, which fully fuses the encoder-decoder features, and extracts multi-scale information. First, a hybrid stride sampling (HSS) block was designed in the encoder to minimize the loss of helpful information caused by the downsampling operation. Second, a dense hybrid dilated convolution (DHDC) was employed in the connection layer. On the premise of preserving feature resolution, it can perceive richer contextual information. Third, a squeeze-and-excitation with residual connections (SERC) was introduced in the decoder to adjust the channel attention adaptively. Finally, we utilized a multi-layer feature fusion mechanism in the skip connection part, which enables the network to consider both low-level details and high-level semantic information. We evaluated the proposed method on three public datasets DRIVE, STARE and CHASE. In the experimental results, the Area under the receiver operating characteristic (ROC) and the accuracy rate (Acc) achieved high performances of 98.62%/97.02%, 98.60%/97.76% and 98.73%/97.38%, respectively. Experimental results show that the proposed method can obtain comparable segmentation performance compared with the state-of-the-art (SOTA) methods. Specifically, the proposed method can effectively reduce the small blood vessel segmentation error, thus proving it a promising tool for auxiliary diagnosis of ophthalmic diseases.

Retinal vessel segmentation based on multi-scale feature and style transfer.

Retinal vessel segmentation is very important for diagnosing and treating certain eye diseases. Recently, many deep learning-based retinal vessel segmentation methods have been proposed; however, there are still many shortcomings (e.g., they cannot obtain satisfactory results when dealing with cross-domain data or segmenting small blood vessels). To alleviate these problems and avoid overly complex models, we propose a novel network based on a multi-scale feature and style transfer (MSFST-NET) for retinal vessel segmentation. Specifically, we first construct a lightweight segmentation module named MSF-Net, which introduces the selective kernel (SK) module to increase the multi-scale feature extraction ability of the model to achieve improved small blood vessel segmentation. Then, to alleviate the problem of model performance degradation when segmenting cross-domain datasets, we propose a style transfer module and a pseudo-label learning strategy. The style transfer module is used to reduce the style difference between the source domain image and the target domain image to improve the segmentation performance for the target domain image. The pseudo-label learning strategy is designed to be combined with the style transfer module to further boost the generalization ability of the model. Moreover, we trained and tested our proposed MSFST-NET in experiments on the DRIVE and CHASE_DB1 datasets. The experimental results demonstrate that MSFST-NET can effectively improve the generalization ability of the model on cross-domain datasets and achieve improved retinal vessel segmentation results than other state-of-the-art methods.

Retrospective multicenter registry for endovascular treatment with newer devices in over 25-cm femoropopliteal artery disease: A retrospective observational study.

Endovascular therapy (EVT) is recommended in femoropopliteal (FP) lesions shorter than 25 cm by current guidelines; however, diffuse FP lesions remains challenging for EVT. The aim of this study was to investigate the efficacy of EVT with the latest devices for FP lesions longer than 25 cm. This retrospective multicenter registry analyzed patients presented peripheral artery disease (PAD) having FP lesions longer than 25 cm who underwent EVT using the latest devices between 2017 and 2021. The primary outcome was restenosis 1 year after EVT. The present study enrolled a total of 504 PAD patients with 614 lesions undergoing EVT for diffuse FP lesions. The Kaplan-Meier analysis showed that the rates of freedom from restenosis and clinically-driven target lesion revascularization were 79.3% and 82.4% 1 year after EVT, respectively. The multivariate Cox proportional hazards regression analysis showed that clinical features associated independently with restenosis risk were cilostazol use (adjusted hazard ratio, 0.49 [0.32-0.74]; p = 0.001), reference vessel diameter (RVD) (0.72 [0.58-0.89] per 1-mm increase; p = 0.002), and P3 segment involvement (2.08 [1.33-3.26]; p = 0.001). The Kaplan-Meier analysis was conducted to compare the primary patency between cases with and without a small RVD, P3 involvement, and/or lack of cilostazol; any risk factors were related to a worse primary patency rate, compared with cases without risk factors. In the current EVT era, the primary patency at 1 year was acceptable at 79.3% in patients with FP lesions longer than 25 cm. A small vessel and P3 segment involvement might be associated with a poor 1-year patency rate after EVT, whereas cilostazol administration might contribute to reducing restenosis.

MF2ResU-Net: a multi-feature fusion deep learning architecture for retinal blood vessel segmentation

ObjectiveFor computer-aided Chinese medical diagnosis and aiming at the problem of insufficient segmentation, a novel multi-level method based on the multi-scale fusion residual neural network (MF2ResU-Net) model is proposed. MethodsTo obtain refined features of retinal blood vessels, three cascade connected U-Net networks are employed. To deal with the problem of difference between the parts of encoder and decoder, in MF2ResU-Net, shortcut connections are used to combine the encoder and decoder layers in the blocks. To refine the feature of segmentation, atrous spatial pyramid pooling (ASPP) is embedded to achieve multi-scale features for the final segmentation networks. ResultsThe MF2ResU-Net was superior to the existing methods on the criteria of sensitivity (Sen), specificity (Spe), accuracy (ACC), and area under curve (AUC), the values of which are 0.8013 and 0.8102, 0.9842 and 0.9809, 0.9700 and 0.9776, and 0.9797 and 0.9837, respectively for DRIVE and CHASE DB1. The results of experiments demonstrated the effectiveness and robustness of the model in the segmentation of complex curvature and small blood vessels. ConclusionBased on residual connections and multi-feature fusion, the proposed method can obtain accurate segmentation of retinal blood vessels by refining the segmentation features, which can provide another diagnosis method for computer-aided Chinese medical diagnosis.

A multi-scale interactive U-Net for pulmonary vessel segmentation method based on transfer learning

Pulmonary vessel segmentation is the key application of AI in lung disease diagnosis and surgical planning. Compared with manual labeling, automatic labeling of pulmonary vessels using an AI-based medical image segmentation method has the advantages of low cost, high accuracy, and efficiency, which is the development trend of medical images. In terms of pulmonary vessel segmentation, FCN and U-Net are the most widely used pulmonary vessel segmentation methods based on deep learning. However, the precision of pulmonary vessel segmentation, especially the small vessels, tends to be poor by such methods. Therefore, to solve the above problem, Multi-Scale Interactive U-Net (MSI-U-Net) is proposed. In MSI-U-Net, three decoder branches are used to extract small-scale, middle-scale and large-scale vessels respectively, which can improve the accuracy of small vessel segmentation by enhancing the representational ability of small vessels. In addition, to solve the problem of small vessel information loss caused by down-sampling, we introduce the attention mechanism into the skip-layer connection and propose a cross-layer aggregation module (CLA). Among the three decoder branches, a multi-scale information interaction strategy (MSIIS) is proposed based on transfer learning, which can effectively enhance the correlation of multi-scale vessels in lung CT images. In the training stage, we propose a scale-induced supervision strategy (SISS). This strategy uses the idea of fusion first and then supervision, which effectively solves the problem of inconsistency in multi-scale vessels classification, thereby reducing the segmentation errors. Finally, we use feature transmission instead of convolution parameter sharing to realize the multi-scale information interaction strategy, and propose an extension scheme called Multi-Level Cascade Interactive U-Net (MLCI-U-Net). The experimental results indicate that our MSI-U-Net and MLCI-U-Net have better performance than other state-of-the-art methods on pulmonary vessel segmentation. Specifically, the best Dice similarity coefficient (DSC), Sensitivity and Precision are obtained by the proposed methods to segment pulmonary vessels.

Coronary computed tomography angiography based endothelial wall shear stress in normal coronary arteries

Abstract Background Endothelial wall shear stress (ESS) is a biomechanical force which plays a key role in the formation and evolution of atherosclerotic lesions. Purpose This study aims to evaluate ESS in coronary arteries without atherosclerosis, and to assess various factors affecting ESS values. Methods Coronary computed tomography angiography (CCTA) images from patients with suspected coronary artery disease were analyzed to identify coronary arteries without atherosclerosis. Minimal and maximal ESS values were calculated for 3-mm segments with dedicated software. Segments were categorized according to lumen diameter tertiles into small (<2.6 mm), intermediate (2.6–3.2 mm) or large (≥3.2 mm) size classes. Normal ranges of minimal and maximal ESS values were calculated per vessel and vessel size. Results A total of 349 normal vessels from 168 patients (mean age 59.4±9.2 years, 39% men) were included. ESS was highest in the left anterior descending artery compared to the left circumflex and the right coronary arteries (2.3 Pa vs. 1.9 Pa vs. 1.6 Pa for minimal ESS, p<0.001 and 3.7 Pa vs. 3.0 Pa vs. 2.5 Pa for maximal ESS, p<0.001). ESS values were highest in small vessel segments compared to intermediate or large segments (3.8 Pa vs. 1.7 Pa vs. 1.2 Pa for minimal ESS, p<0.001 and 6.0 Pa vs. 2.6 Pa vs. 2.0 Pa for maximal ESS, p<0.001). Weak to moderate correlation was found between ESS and the distance from the ostium (ρ values ranging from 0.22 to 0.62 for different vessels). Conclusion We derived ESS values from the CCTA images for visually normal coronary arteries. ESS values depend strongly on the lumen diameter of the coronary vessel. The normal ranges of minimal and maximal ESS can be used in future studies, where ESS values in stenotic lesions are compared to the normal values derived in the present analysis. Funding Acknowledgement Type of funding sources: Foundation. Main funding source(s): Finnish Foundation for Cardiovascular Research

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.

HPM-Net: Hierarchical progressive multiscale network for liver vessel segmentation in CT images

Background and objectiveThe segmentation and visualization of liver vessels in 3D CT images are essential for computer-aided diagnosis and preoperative planning of liver diseases. Due to the irregular structure of liver vessels and image noise, accurate extraction of liver vessels is difficult. In particular, accurate segmentation of small vessels is always a challenge, as multiple single down-sampling usually results in a loss of information. MethodsIn this paper, we propose a hierarchical progressive multiscale learning network (HPM-Net) framework for liver vessel segmentation. Firstly, the hierarchical progressive multiscale learning network combines internal and external progressive learning methods to learn semantic information about liver vessels at different scales by acquiring receptive fields of different sizes. Secondly, to better capture vessel features, we propose a dual-branch progressive 3D Unet, which uses a dual-branch progressive (DBP) down-sampling strategy to reduce the loss of detailed information in the process of network down-sampling. Finally, a deep supervision mechanism is introduced into the framework and backbone network to speed up the network convergence and achieve better training of the network. ResultsWe conducted experiments on the public dataset 3Dircadb for liver vessel segmentation. The average dice coefficient and sensitivity of the proposed method reached 75.18% and 78.84%, respectively, both higher than the original network. ConclusionExperimental results show that the proposed hierarchical progressive multiscale network can accurately segment the labeled liver vessels from the CT images.

MR‐UNet: An UNet model using multi‐scale and residual convolutions for retinal vessel segmentation

AbstractThe overall performance of the retinal vessel segmentation network based on improved UNet is excellent, but there is still room for improvement in the small blood vessel segmentation. Therefore, this paper proposes an improved MR‐UNet, which designs two new blocks: the multi‐scale convolution (Multiconv) block and the residual convolution (Resconv) block. The Multiconv block uses different size convolution kernels to extract the characteristics of different thicknesses of retinal blood vessels, thereby improving the model's ability to segment small blood vessels. The Resconv block uses different convolutional layers to process the shallow semantic information in the encoding stage and then concatenates it to the decoding stage, reducing the semantic difference between the encoder and the decoder. On the retinal data sets DRIVE, STARE and CHASE_DB1, the accuracy (Acc) of this model is 0.9705, 0.9747, 0.9778, the specificity (Sp) is 0.9863, 0.9892, 0.9930, the AUC is 0.9872, 0.9849, 0.9925, and the F1‐Score is 0.8270, 0.8134, 0.8460, respectively. Compared with the original UNet, Se, Sp, and F1‐Score of MR‐UNet increase by 2.5%, 0.5%, and 0.4%, respectively, proving that MR‐UNet has better comprehensive performance.

Evaluation of Fundus Blood Flow Perfusion in Patients with Diabetic Retinopathy after PPV with Fundus Color Doppler Based on Big Data Mining.

In this paper, we have carefully investigated the clinical phenotype and genotype of patients with Johanson-Blizzard syndrome (JBS) with diabetes mellitus as the main manifestation. Retinal vessel segmentation is an important tool for the detection of many eye diseases and plays an important role in the automated screening system for retinal diseases. A segmentation algorithm based on a multiscale attentional resolution network is proposed to address the problem of insufficient segmentation of small vessels and pathological missegmentation in existing methods. The network is based on the encoder-decoder architecture, and the attention residual block is introduced in the submodule to enhance the feature propagation ability and reduce the impact of uneven illumination and low contrast on the model. The jump connection is added between the encoder and decoder, and the traditional pooling layer is removed to retain sufficient vascular detail information. Two multiscale feature fusion methods, parallel multibranch structure, and spatial pyramid pooling are used to achieve feature extraction under different sensory fields. We collected the clinical data, laboratory tests, and imaging examinations of JBS patients, extracted the genomic DNA of relevant family members, and validated them by whole-exome sequencing and Sanger sequencing. The patient had diabetes mellitus as the main manifestation, with widened eye spacing, low flat nasal root, hypoplastic nasal wing, and low hairline deformities. Genetic testing confirmed the presence of a c.4463 T > C (p.Ile1488Thr) pure missense mutation in the UBR1 gene, which was a novel mutation locus, and pathogenicity analysis indicated that the locus was pathogenic. This patient carries a new UBR1 gene c.4463 T > C pure mutation, which improves the clinical understanding of the clinical phenotypic spectrum of JBS and broadens the genetic spectrum of the UBR1 gene. The experimental results showed that the method achieved 83.26% and 82.56% F1 values on CHASEDB1 and STARE standard sets, respectively, and 83.51% and 81.20% sensitivity, respectively, and its performance was better than the current mainstream methods.

Indirect Flow Diversion for Off-Centered Bifurcation Aneurysms and Distant Small-Vessel Aneurysms, a Retrospective Proof of Concept Study From Five Neurovascular Centers.

Background: Treatment of cerebral aneurysms using hemodynamic implants such as endosaccular flow disruptors and endoluminal flow diverters has gained significant momentum during recent years. The intended target zone of those devices is the immediate interface between aneurysm and parent vessel. The therapeutic success is based on the reduction of aneurysmal perfusion and the subsequent formation of a neointima along the surface of the implant. However, a subset of aneurysms–off-centered bifurcation aneurysms involving the origin of efferent branches and aneurysms arising from peripheral segments of small cerebral vessels–oftentimes cannot be treated via coiling or implanting a hemodynamic implant at the neck level for technical reasons. In those cases, indirect flow diversion–a flow diverter deployed in the main artery proximal to the parent vessel of the aneurysm–can be a viable treatment strategy, but clinical evidence is lacking in this regard.Materials and Methods: Five neurovascular centers contributed to this retrospective analysis of patients who were treated with indirect flow diversion. Clinical data, aneurysm characteristics, anti-platelet medication, and follow-up results, including procedural and post-procedural complications, were recorded.Results: Seventeen patients (mean age: 60.5 years, range: 35–77 years) with 17 target aneurysms (vertebrobasilar: n = 9) were treated with indirect flow diversion. The average distance between the flow-diverting stent and the aneurysm was 1.65 mm (range: 0.4–2.4 mm). In 15/17 patients (88.2%), perfusion of the aneurysm was reduced immediately after implantation. Follow-ups were available for 12 cases. Delayed opacification (OKM A3: 11.8%), reduction in size (OKM B1-3: 29.4%) and occlusion (D1: 47.1%) were observable at the latest investigation. Clinically relevant procedural complications and adverse events in the early phase and in the late subacute phase were not observed in any case.Conclusion: Our preliminary data suggest that indirect flow diversion is a safe, feasible, and effective approach to off-centered bifurcation aneurysms and distant small-vessel aneurysms. However, validation with larger studies, including long-term outcomes and optimized imaging, is warranted.

Blood Vessel Segmentation of Retinal Image Based on Dense-U-Net Network.

The accurate segmentation of retinal blood vessels in fundus is of great practical significance to help doctors diagnose fundus diseases. Aiming to solve the problems of serious segmentation errors and low accuracy in traditional retinal segmentation, a scheme based on the combination of U-Net and Dense-Net was proposed. Firstly, the vascular feature information was enhanced by fusion limited contrast histogram equalization, median filtering, data normalization and multi-scale morphological transformation, and the artifact was corrected by adaptive gamma correction. Secondly, the randomly extracted image blocks are used as training data to increase the data and improve the generalization ability. Thirdly, stochastic gradient descent was used to optimize the Dice loss function to improve the segmentation accuracy. Finally, the Dense-U-net model was used for segmentation. The specificity, accuracy, sensitivity and AUC of this algorithm are 0.9896, 0.9698, 0.7931, 0.8946 and 0.9738, respectively. The proposed method improves the segmentation accuracy of vessels and the segmentation of small vessels.

DV-Net: Accurate liver vessel segmentation via dense connection model with D-BCE loss function

Recently, liver vessel segmentation has aroused widespread interest in medical image analysis. Accurately extracting blood vessels from livers is a difficult task due to their complex vessel structures and image noises. To make a neural network better adapt to this complexity, a deeper neural network is required to fit this nonlinear transformation. In particular, accurate segmentation of small blood vessels is always a challenge, since onefold down-sampling usually causes the loss of information. In this study, we introduce a dense block structure into the V-net to construct a new Dense V-Net (DV-Net) and use data augmentation to segment liver vessels from abdominal CT volumes with a few training samples. In addition, we propose a dual-branch dense connection down-sampling strategy (DCDS) to better capture vascular features and a D-BCE loss function to maximize the utilization of image resources. The proposed DV-Net structure is more powerful in the discrimination of vessel and non-vessel areas. We extensively evaluated the proposed method on the datasets of 3Dircadb and MICCAI 2018 Medical Segmentation Decalthon (MSD) Challenge. Experimental results show that the proposed DV-Net significantly improves the average segmentation Dice score. The average Dice score and sensitivity on 3Dircadb were (75.46%) and (76.93%), respectively, which are better than those of existing methods.

Blood Vessel Segmentation Based on the 3D Residual U-Net

In this paper, we propose blood vessel segmentation based on the 3D residual U-Net method. First, we integrate the residual block structure into the 3D U-Net. By exploring the influence of adding residual blocks at different positions in the 3D U-Net, we establish a novel and effective 3D residual U-Net. In addition, to address the challenges of pixel imbalance, vessel boundary segmentation, and small vessel segmentation, we develop a new weighted Dice loss function with a better effect than the weighted cross-entropy loss function. When training the model, we adopted a two-stage method from coarse-to-fine. In the fine stage, a local segmentation method of 3D sliding window is added. In the model testing phase, we used the 3D fixed-point method. Furthermore, we employ the 3D morphological closed operation to smooth the surfaces of vessels and volume analysis to remove noise blocks. To verify the accuracy and stability of our method, we compare our method with FCN, 3D DenseNet, and 3D U-Net. The experimental results indicate that our method has higher accuracy and better stability than the other studied methods and that the average Dice coefficients for hepatic veins and portal veins reach 71.7% and 76.5% in the coarse stage and 72.5% and 77.2% in the fine stage, respectively. In order to verify the robustness of the model, we conducted the same comparative experiment on the brain vessel datasets, and the average Dice coefficient reached 87.2%.

Varying perivascular astroglial endfoot dimensions along the vascular tree maintain perivascular-interstitial flux through the cortical mantle.

The glymphatic system is a recently defined brain-wide network of perivascular spaces along which cerebrospinal fluid (CSF) and interstitial solutes exchange. Astrocyte endfeet encircling the perivascular space form a physical barrier in between these two compartments, and fluid and solutes that are not taken up by astrocytes move out of the perivascular space through the junctions in between astrocyte endfeet. However, little is known about the anatomical structure and the physiological roles of the astrocyte endfeet in regulating the local perivascular exchange. Here, visualizing astrocyte endfoot-endfoot junctions with immunofluorescent labeling against the protein megalencephalic leukoencephalopathy with subcortical cysts-1 (MLC1), we characterized endfoot dimensions along the mouse cerebrovascular tree. We observed marked heterogeneity in endfoot dimensions along vessels of different sizes, and of different types. Specifically, endfoot size was positively correlated with the vessel diameters, with large vessel segments surrounded by large endfeet and small vessel segments surrounded by small endfeet. This association was most pronounced along arterial, rather than venous segments. Computational modeling simulating vascular trees with uniform or varying endfeet dimensions demonstrates that varying endfoot dimensions maintain near constant perivascular-interstitial flux despite correspondingly declining perivascular pressures along the cerebrovascular tree through the cortical depth. These results describe a novel anatomical feature of perivascular astroglial endfeet and suggest that endfoot heterogeneity may be an evolutionary adaptation to maintain perivascular CSF-interstitial fluid exchange through deep brain structures.