Vessel Edges Research Articles

Application of the Full-Width-at-Half-Maximum Image Segmentation Method to Analyse Retinal Vascular Changes in Patients with Diabetic Retinopathy.

This study used spectral domain optical coherence tomography (SD-OCT) and full-width-at-half-maximum image segmentation to investigate the morphological changes of retinal blood vessels in patients with diabetic retinopathy (DR). Seventy-five patients with type 2 diabetes mellitus (T2DM) without DR and 65 patients with DR were studied. Vascular images of superior temporal region B of the retina were obtained by SD-OCT. The edges of retinal vessels were identified by the full-width-at-half-maximum image segmentation method. The lumen diameter, wall thickness (WT), wall cross-sectional area (WCSA), and wall-to-lumen ratio (WLR) were investigated. We found that, compared with the no diabetic retinopathy (NDR) group, patients in the DR group had an increased retinal arteriolar lumen diameter (RALD), retinal arteriolar outer diameter (RAOD), and WT (128.80 μm versus 104.88 μm; 147.01 μm versus 135.60 μm; 18.29 μm versus 15.26 μm, P < 0.05, respectively). The retinal venular lumen diameter (RVLD), retinal venular outer diameter (RVOD), and venular WT in the DR group were also increased (146.17 μm versus 133.66 μm; 180.20 μm versus 156.43 μm; 17.01 μm versus 11.38 μm, P < 0.05, respectively). The morphological changes in retinal vessels were significantly correlated with DR stage. In conclusion, in diabetic patients with DR, both retinal arteries and veins are widened and exhibit increased vascular thickness.

Dual Encoder-Based Dynamic-Channel Graph Convolutional Network With Edge Enhancement for Retinal Vessel Segmentation.

Retinal vessel segmentation with deep learning technology is a crucial auxiliary method for clinicians to diagnose fundus diseases. However, the deep learning approaches inevitably lose the edge information, which contains spatial features of vessels while performing down-sampling, leading to the limited segmentation performance of fine blood vessels. Furthermore, the existing methods ignore the dynamic topological correlations among feature maps in the deep learning framework, resulting in the inefficient capture of the channel characterization. To address these limitations, we propose a novel dual encoder-based dynamic-channel graph convolutional network with edge enhancement (DE-DCGCN-EE) for retinal vessel segmentation. Specifically, we first design an edge detection-based dual encoder to preserve the edge of vessels in down-sampling. Secondly, we investigate a dynamic-channel graph convolutional network to map the image channels to the topological space and synthesize the features of each channel on the topological map, which solves the limitation of insufficient channel information utilization. Finally, we study an edge enhancement block, aiming to fuse the edge and spatial features in the dual encoder, which is beneficial to improve the accuracy of fine blood vessel segmentation. Competitive experimental results on five retinal image datasets validate the efficacy of the proposed DE-DCGCN-EE, which achieves more remarkable segmentation results against the other state-of-the-art methods, indicating its potential clinical application.

MCPANet: Multiscale Cross-Position Attention Network for Retinal Vessel Image Segmentation

Accurate medical imaging segmentation of the retinal fundus vasculature is essential to assist physicians in diagnosis and treatment. In recent years, convolutional neural networks (CNN) are widely used to classify retinal blood vessel pixels for retinal blood vessel segmentation tasks. However, the convolutional block receptive field is limited, simple multiple superpositions tend to cause information loss, and there are limitations in feature extraction as well as vessel segmentation. To address these problems, this paper proposes a new retinal vessel segmentation network based on U-Net, which is called multi-scale cross-position attention network (MCPANet). MCPANet uses multiple scales of input to compensate for image detail information and applies to skip connections between encoding blocks and decoding blocks to ensure information transfer while effectively reducing noise. We propose a cross-position attention module to link the positional relationships between pixels and obtain global contextual information, which enables the model to segment not only the fine capillaries but also clear vessel edges. At the same time, multiple scale pooling operations are used to expand the receptive field and enhance feature extraction. It further reduces pixel classification errors and eases the segmentation difficulty caused by the asymmetry of fundus blood vessel distribution. We trained and validated our proposed model on three publicly available datasets, DRIVE, CHASE, and STARE, which obtained segmentation accuracy of 97.05%, 97.58%, and 97.68%, and Dice of 83.15%, 81.48%, and 85.05%, respectively. The results demonstrate that the proposed method in this paper achieves better results in terms of performance and segmentation results when compared with existing methods.

Abstract 347: Using Deep Convolutional Neural Networks To Automate Classification Of Carotid Plaques From Ultrasound Imaging

Background: Stroke is a devastating consequence of plaque rupture from the carotid arteries. Current management of carotid plaques involves waiting for symptoms (e.g., stroke or mini-stroke), as intervention itself has risk of stroke and not all plaques are vulnerable to rupture. There is a need to better risk-stratify plaque that causes stroke. Carotid ultrasound (US) is a non-invasive and inexpensive visualization of plaques but is limited by human interpretation. We hypothesize that convolutional neural networks (CNNs) will identify unique features of carotid plaques for automated risk stratification. Methods: Our workflow is illustrated in Fig. a. A total of 141 B-mode US images of carotid arteries were included; 64 high-risk with symptomatic carotid plaques and 75 low-risk with no significant plaque. Data was cropped and divided into training (70%) and holdout test (30%) subsets. During model training, an ensemble of ResNet-18 CNNs learned classification of low-risk and high-risk cases using five-fold stratified cross validation and was used to predict on the holdout test set. The model was evaluated using ROC-AUC and sensitivity. Saliency maps were used for model interpretability to highlight relevant pixels for model decisions. Results: The cross-validation AUC was 0.995 ± 0.010. The testing AUC was 0.909 and class-wise sensitivities were 0.88 (low-risk) and 0.79 (high-risk). The density plot (Fig. b) shows that the classifier correctly identifies both classes with confidence. Model interpretability using saliency maps (Fig. c) shows pixels corresponding to carotid artery vessel edges and in high-risk cases, carotid plaques. Conclusions: Using this proof-of-concept model, carotid US long axis images are sufficient to identify high-risk plaques in symptomatic patients - we now need to determine whether we can identify high-risk plaques before symptoms to prevent devastating stroke caused by carotid disease.

DBFU-Net: Double branch fusion U-Net with hard example weighting train strategy to segment retinal vessel.

BackgroundMany fundus imaging modalities measure ocular changes. Automatic retinal vessel segmentation (RVS) is a significant fundus image-based method for the diagnosis of ophthalmologic diseases. However, precise vessel segmentation is a challenging task when detecting micro-changes in fundus images, e.g., tiny vessels, vessel edges, vessel lesions and optic disc edges.MethodsIn this paper, we will introduce a novel double branch fusion U-Net model that allows one of the branches to be trained by a weighting scheme that emphasizes harder examples to improve the overall segmentation performance. A new mask, we call a hard example mask, is needed for those examples that include a weighting strategy that is different from other methods. The method we propose extracts the hard example mask by morphology, meaning that the hard example mask does not need any rough segmentation model. To alleviate overfitting, we propose a random channel attention mechanism that is better than the drop-out method or the L2-regularization method in RVS.ResultsWe have verified the proposed approach on the DRIVE, STARE and CHASE datasets to quantify the performance metrics. Compared to other existing approaches, using those dataset platforms, the proposed approach has competitive performance metrics. (DRIVE: F1-Score = 0.8289, G-Mean = 0.8995, AUC = 0.9811; STARE: F1-Score = 0.8501, G-Mean = 0.9198, AUC = 0.9892; CHASE: F1-Score = 0.8375, G-Mean = 0.9138, AUC = 0.9879).DiscussionThe segmentation results showed that DBFU-Net with RCA achieves competitive performance in three RVS datasets. Additionally, the proposed morphological-based extraction method for hard examples can reduce the computational cost. Finally, the random channel attention mechanism proposed in this paper has proven to be more effective than other regularization methods in the RVS task.

Marker trajectory assessment in optical cone beam computed tomography scanner geometry

Measured trajectories of markers on vessel wall and beads on a plumb are compared with calculated trajectories for transmission images obtained with Vista16™ optical cone beam scanner. Effective pixel size was 0.11 mm. An algorithm was developed to measure trajectories for the plumb line phantom and a semi-automatic version was required for the gel filled vessels due to overlap of markers and seam projections at maximum lateral positions. It was found that for 15 cm diameter vessels, the vessel edge was not visible due to divergence in cone beam geometry. Accounting for this effect was necessary in order to remove artefacts from calculated vessel trajectory. Displacement differences were found to be less than 0.1 mm for the plumb line, 0.3 mm for a beads and 1 mm for markers on gel filled vessel. Minimization of the vessel marker trajectory deviation will provide a more accurate approach to refractive index optimization. No optical aberrations were detected from the trajectory analysis and manufacturer scanner alignment and geometrical calibrations were independently verified.

Curvelet Transform Based on Edge Preserving Filter for Retinal Blood Vessel Segmentation

Segmentation of vessel in retinal fundus images is a primary step for the clinical identification for specific eye diseases. Effective diagnosis of vascular pathologies from angiographic images is thus a vital aspect and generally depends on segmentation of vascular structure. Although various approaches for retinal vessel segmentation are extensively utilized, however, the responses are lower at vessel's edges. The curvelet transform signifies edges better than wavelets, and hence convenient for multiscale edge enhancement. The bilateral filter is a nonlinear filter that is capable of providing effective smoothing while preserving strong edges. Fast bilateral filter is an advanced version of bilateral filter that regulates the contrast while preserving the edges. Therefore, in this paper a fusion algorithm is recommended by fusing fast bilateral filter that can effectively preserve the edge details and curvelet transform that has better capability to detect the edge direction feature and better investigation and tracking of significant characteristics of the image. Afterwards C mean thresholding is used for the extraction of vessel. The recommended fusion approach is assessed on DRIVE dataset. Experimental results illustrate that the fusion algorithm preserved the advantages of the both and provides better result. The results demonstrate that the recommended method outperforms the traditional approaches.

Edge-aware U-net with gated convolution for retinal vessel segmentation

Morphological changes of retinal vessels, especially tiny vessels play an essential role in the diagnosis and clinical prognosis of specific cardiovascular and ophthalmic diseases. However, the precise segmentation of retinal vessels, particular the vessel terminals of the retinal vascular branches, tends to be poor. In this proposed approach, we introduce new edge-aware flows into U-Net encoder-decoder architecture to guide the retinal vessel segmentation, which makes segmentation more sensitive to the fine edges of the capillaries. The edge-gated flow with gated convolution only focuses on edge presentation and learns to emphasize the vessel edges using features extracted from the encoder path, and exports the edge prediction results. The edge-downsamling flow then extracts the edge features from the edge prediction results and feeds them back into the decoder path to refine the segmentation results. The proposed method achieves state-of-the-art performance in term of accuracy of 0.9701, 0.9691, and 0.9811 and gains an increment of 0.0056, 0.0026, and 0.0047 compared with U-Net baseline on three publicly available datasets: DRIVE, STARE, and CHASEDB1, respectively. The experimental results show that the proposed Edge-Aware U-Net is an effective architecture that provides more accurate segmentation around the vessel edges and significantly boosts the performance on tiny vessels.

Interactive Blood Vessel Segmentation from Retinal Fundus Image Based on Canny Edge Detector.

Optometrists, ophthalmologists, orthoptists, and other trained medical professionals use fundus photography to monitor the progression of certain eye conditions or diseases. Segmentation of the vessel tree is an essential process of retinal analysis. In this paper, an interactive blood vessel segmentation from retinal fundus image based on Canny edge detection is proposed. Semi-automated segmentation of specific vessels can be done by simply moving the cursor across a particular vessel. The pre-processing stage includes the green color channel extraction, applying Contrast Limited Adaptive Histogram Equalization (CLAHE), and retinal outline removal. After that, the edge detection techniques, which are based on the Canny algorithm, will be applied. The vessels will be selected interactively on the developed graphical user interface (GUI). The program will draw out the vessel edges. After that, those vessel edges will be segmented to bring focus on its details or detect the abnormal vessel. This proposed approach is useful because different edge detection parameter settings can be applied to the same image to highlight particular vessels for analysis or presentation.

Mise au point sur l’anatomie de la région axillaire : l’arc musculaire du grand dorsal ou muscle de Carl Langer, un grand oublié dans la formation des internes

ObjectiveThe Carl Langer muscle is the main anatomical variation of the walls of the axillary area, its incidence being about 7%. The presence of this muscle crossing the anterior edge of the axillary vessels can induce difficulties of exposure, location and dissection during axillary surgery. In addition, it may be responsible for primary lymphedema of the upper limb, venous thrombosis of the axillary vein or thoracic outlet syndrome due to vascular or nervous compression. The objective of this work was to evaluate the state of knowledge on Carl Langer muscle of the gynecology-obstetrics medical residents of the French Eastern Region. Material and methodsAll the medical residents enrolled in the specialized diploma in gynecology-obstetrics in the 5 regions (Alsace, Bourgogne, Lorraine, Champagne-Ardenne and Franche-Comté) were questioned by means of a questionnaire sent by e-mail. ResultsFrom February to March 2021, 94 of the 160 medical residents interviewed answered to the questionnaire. Ninety-one of them (97%) did not know Carl Langer's muscle. Three medical residents thought they knew this muscle (3%) but their knowledge was imperfect. ConclusionOur work has highlighted the general lack of knowledge of this anatomical variation, which is relatively frequent, among French gynecology-obstetrics medical residents who are required to examine or perform surgery on this area. This updated review of the literature should optimize the knowledge of the anatomy of the axillary area and consequently its surgery.

Recommendations and Clinical Validation of Inguinal Clinical Target Volume Delineation in Penile Cancer

This study determined the patterns of distribution of inguinal nodes and the optimal margin needed around the femoral vessels to propose guidelines for inguinal clinical target volume (CTV) delineation in penile cancer. Patients with biopsy-proven penile squamous cell carcinoma with inguinal node metastases were identified on a computed tomography scan. Distribution of nodes and distance of nodes to the nearest femoral vessel and inferior edge of pubic symphysis (IPS) were obtained. A 3-dimensional CTV margin to cover 95% of the nodes was estimated to evolve contouring guidelines. A comprehensive contouring guide with a visual atlas for inguinal CTV delineation was created. The proposed CTV was prospectively validated in 14 consecutive patients with penile squamous cell carcinoma treated with post-operative radiation therapy to the inguino-femoral region. Of the 222 inguinal lymph nodes analyzed in 33 patients, 99% were located anterior (81% antero-medial, 18% antero-lateral) to femoral vessels. A majority (95%) of the antero-laterally located nodes were superior to IPS. Margin around the femoral vessels to cover 95% of the nodes for supra-IPS region in anterior, medial, and lateral directions was 29, 23, and 21 mm, respectively, and for infra-IPS region in anterior and medial directions was 21 and 23 mm, respectively. Cranial and caudal extent of CTV delineation with respect to IPS was at 48 and 50 mm, respectively for 95% nodal coverage. On prospective validation, after a median follow-up of 24 months (range, 5-31), none of the patients developed marginal recurrences. Grade 1 and grade 2 lymphedema was seen in 6 (42.8%) and 4 (28.5%) patients, respectively. This study analyzed pattern of distribution of inguinal nodes and proposes guidelines for inguinal CTV delineation in penile cancer. The proposed guidelines were validated prospectively in a small cohort of patients and found to be implementable and safe.

Image processing pipeline for the detection of blood flow through retinal vessels with subpixel accuracy in fundus images

Background and ObjectiveBlood flow detection through the retinal vessels is an essential step in diagnosing several eye diseases. It manifests itself as changes in vessel diameter in consecutive phases of blood flow. Previous studies focused mainly on determining retinal vessel diameter by the manual demarcation of vessel edges, which is time-consuming. As a result, only a few selected vessels were considered, which is not reliable. Such measurements are also prone to human errors and operator subjectivity, which additionally decreases their reliability. For these reasons, this paper proposes an automated pipeline to analyze the blood flow through retinal vessels. MethodsConvolutional neural networks were used for optic disc and vessel detection and full width at half maximum analysis used for vessel width assessment at the subpixel level. Measurements of the diameter were performed for five phases of the blood flow to all vessels at a particular distance derived from the optic disc size. We tested the approach on fundus images of five patients, with both eyes examined in each participant. The threshold for the detections of blood flow was when the retinal diameter vessel measurements were above the camera's resolution as compared among all 5 phases of blood flow. ResultsA total of 205 large caliber blood vessels were analyzed with blood flow detected in 18 retinal blood vessels. Conclusions Average vessel diameters derived from manual and automatic measurements differed on average by 4.96%. Average relative errors for single vessel measurements along the vessels range from 4.21 to 11.85%, with a global average at the level of 8%. Therefore, the measurements can be considered as accurate and in a high agreement between the expert and algorithm

Automatic Artery/Vein Classification Using a Vessel-Constraint Network for Multicenter Fundus Images.

Retinal blood vessel morphological abnormalities are generally associated with cardiovascular, cerebrovascular, and systemic diseases, automatic artery/vein (A/V) classification is particularly important for medical image analysis and clinical decision making. However, the current method still has some limitations in A/V classification, especially the blood vessel edge and end error problems caused by the single scale and the blurred boundary of the A/V. To alleviate these problems, in this work, we propose a vessel-constraint network (VC-Net) that utilizes the information of vessel distribution and edge to enhance A/V classification, which is a high-precision A/V classification model based on data fusion. Particularly, the VC-Net introduces a vessel-constraint (VC) module that combines local and global vessel information to generate a weight map to constrain the A/V features, which suppresses the background-prone features and enhances the edge and end features of blood vessels. In addition, the VC-Net employs a multiscale feature (MSF) module to extract blood vessel information with different scales to improve the feature extraction capability and robustness of the model. And the VC-Net can get vessel segmentation results simultaneously. The proposed method is tested on publicly available fundus image datasets with different scales, namely, DRIVE, LES, and HRF, and validated on two newly created multicenter datasets: Tongren and Kailuan. We achieve a balance accuracy of 0.9554 and F1 scores of 0.7616 and 0.7971 for the arteries and veins, respectively, on the DRIVE dataset. The experimental results prove that the proposed model achieves competitive performance in A/V classification and vessel segmentation tasks compared with state-of-the-art methods. Finally, we test the Kailuan dataset with other trained fusion datasets, the results also show good robustness. To promote research in this area, the Tongren dataset and source code will be made publicly available. The dataset and code will be made available at https://github.com/huawang123/VC-Net.

Edge-competing Pathological Liver Vessel Segmentation with Limited Labels

The microvascular invasion (MVI) is a major prognostic factor in hepatocellular carcinoma, which is one of the malignant tumors with the highest mortality rate. The diagnosis of MVI needs discovering the vessels that contain hepatocellular carcinoma cells and counting their number in each vessel, which depends heavily on experiences of the doctor, is largely subjective and time-consuming. However, there is no algorithm as yet tailored for the MVI detection from pathological images. This paper collects the first pathological liver image dataset containing $522$ whole slide images with labels of vessels, MVI, and hepatocellular carcinoma grades. The first and essential step for the automatic diagnosis of MVI is the accurate segmentation of vessels. The unique characteristics of pathological liver images, such as super-large size, multi-scale vessel, and blurred vessel edges, make the accurate vessel segmentation challenging. Based on the collected dataset, we propose an Edge-competing Vessel Segmentation Network (EVS-Net), which contains a segmentation network and two edge segmentation discriminators. The segmentation network, combined with an edge-aware self-supervision mechanism, is devised to conduct vessel segmentation with limited labeled patches. Meanwhile, two discriminators are introduced to distinguish whether the segmented vessel and background contain residual features in an adversarial manner. In the training stage, two discriminators are devised to compete for the predicted position of edges. Exhaustive experiments demonstrate that, with only limited labeled patches, EVS-Net achieves a close performance of fully supervised methods, which provides a convenient tool for the pathological liver vessel segmentation. Code is publicly available at https://github.com/wang97zh/EVS-Net.

Gradient based ant spread modification on ant colony optimization method for retinal blood vessel edge detection

Retinal blood vessels are a very important object to diagnosing illness. Fast, automatic, adaptive, and accurate systems to segment the retinal vessels are particularly useful. The retinal blood vessels can be detected by edge detection. This research will compare the edge detection techniques with adaptive ant colony optimization (Adaptive ACO). Generally, the early ants on the conventional ant colony optimization (ACO) are randomly distributed. This condition can cause the ant distribution imbalances. Based on this problem, the ant distribution modification on ACO is proposed to optimize the ant placement based on the gradient. The gradient values are used to determine the ants’ placement. The ants are not randomly distributed but placed in the highest gradient. The so-called Adaptive ACO is expected to be used for better and faster path discovery optimization. The average PSNR of Prewitt edges, Sobel edges, conventional ACO, and the proposed method are 11.605, 11.913, 9.783, and 15.874 respectively. The PSNR of the proposed method has the greatest value than others. It shows that the placement of ants based on gradient can improve edge detection accuracy. The application of the Adaptive ACO method has successfully optimized the result of retinal vessel edge detection.

Noise and spatial resolution properties of a commercially available deep learning-based CT reconstruction algorithm.

To characterize the noise and spatial resolution properties of a commercially available deep learning-based computed tomography (CT) reconstruction algorithm. Two phantom experiments were performed. The first used a multisized image quality phantom (Mercury v3.0, Duke University) imaged at five radiation dose levels (CTDIvol : 0.9, 1.2, 3.6, 7.0, and 22.3mGy) with a fixed tube current technique on a commercial CT scanner (GE Revolution CT). Images were reconstructed with conventional (FBP), iterative (GE ASiR-V), and deep learning-based (GE True Fidelity) reconstruction algorithms. Noise power spectrum (NPS), high-contrast (air-polyethylene interface), and intermediate-contrast (water-polyethylene interface) task transfer functions (TTF) were measured for each dose level and phantom size and summarized in terms of average noise frequency (fav ) and frequency at which the TTF was reduced to 50% (f50% ), respectively. The second experiment used a custom phantom with low-contrast rods and lung texture sections for the assessment of low-contrast TTF and noise spatial distribution. The phantom was imaged at five dose levels (CTDIvol : 1.0, 2.1, 3.0, 6.0, and 10.0mGy) with 20 repeated scans at each dose, and images reconstructed with the same reconstruction algorithms. The local noise stationarity was assessed by generating spatial noise maps from the ensemble of repeated images and computing a noise inhomogeneity index, , following AAPM TG233 methods. All measurements were compared among the algorithms. Compared to FBP, noise magnitude was reduced on average (± one standard deviation) by 74±6% and 68±4% for ASiR-V (at "100%" setting) and True Fidelity (at "High" setting), respectively. The noise texture from ASiR-V had substantially lower noise frequency content with 55±4% lower NPS fav compared to FBP while True Fidelity had only marginally different noise frequency content with 9±5% lower NPS fav compared to FBP. Both ASiR-V and True Fidelity demonstrated locally nonstationary noise in a lung texture background at all radiation dose levels, with higher noise near high-contrast edges of vessels and lower noise in uniform regions. At the 1.0mGy dose level values were 314% and 271% higher in ASiR-V and True Fidelity compared to FBP, respectively. High-contrast spatial resolution was similar between all algorithms for all dose levels and phantom sizes (<3% difference in TTF f50% ). Compared to FBP, low-contrast spatial resolution was lower for ASiR-V and True Fidelity with a reduction of TTF f50% of up to 42% and 36%, respectively. The deep learning-based CT reconstruction demonstrated a strong noise magnitude reduction compared to FBP while maintaining similar noise texture and high-contrast spatial resolution. However, the algorithm resulted in images with a locally nonstationary noise in lung textured backgrounds and had somewhat degraded low-contrast spatial resolution similar to what has been observed in currently available iterative reconstruction techniques.

SUD-GAN: Deep Convolution Generative Adversarial Network Combined with Short Connection and Dense Block for Retinal Vessel Segmentation.

Since morphology of retinal blood vessels plays a key role in ophthalmological disease diagnosis, retinal vessel segmentation is an indispensable step for the screening and diagnosis of retinal diseases with fundus images. In this paper, deep convolution adversarial network combined with short connection and dense block is proposed to separate blood vessels from fundus image, named SUD-GAN. The generator adopts U-shape encode-decode structure and adds short connection block between convolution layers to prevent gradient dispersion caused by deep convolution network. The discriminator is all composed of convolution block, and dense connection structure is added to the middle part of the convolution network to strengthen the spread of features and enhance the network discrimination ability. The proposed method is evaluated on two publicly available databases, the DRIVE and STARE. The results show that the proposed method outperforms the state-of-the-art performance in sensitivity and specificity, which were 0.8340 and 0.9820, and 0.8334 and 0.9897 respectively on DRIVE and STARE, and can detect more tiny vessels and locate the edge of blood vessels more accurately.

Large and Uneven Bites in End-to-End Anastomosis of the Rat Femoral Artery.

Successful microvascular anastomosis depends on sutures that adequately oppose both cut vessel edges. Trainees tend to take oversized or uneven bite. To improve early microsurgical skill acquisition using the rat, this study tests the belief that such bites compromise early patency by applying exaggerated bites to end-to-end arterial anastomoses. Twelve Sprague-Dawley rats were randomly assigned to one of the four bite techniques to be applied to both femoral arteries (mean diameter, 0.8 mm). Large (L) and standard (S) bites measured 1.0 and 0.2 mm from the edge, respectively. Eight simple interrupted anastomoses were performed per bite technique, each labeled according to every proximal end bite size, followed by every distal end bite size: LL, LS, SL, and SS. Anastomosis time and blood flow rates were recorded and analyzed statistically. After sacrifice 5 days postoperation, anastomosis sections of each technique were examined histologically. All 24 anastomoses (100%) maintained patency for 5 days. There was no statistical difference between all postoperative blood flow measurements at any given time. Anastomosis times using LL, LS, SL, and SS bite techniques were 41.6, 33.2, 34.8, and 25.5 minutes, respectively. Anastomosis time for the traditional bite technique (SS) was significantly shorter than all other bite techniques (p < 0.05). Histological examination of the harvested segments from each group revealed similar pathophysiological features. Oversized bites (1 mm), placed symmetrically and asymmetrically across the anastomosis, do not affect early patency in the rat femoral artery. A reduced reliance on conventional guidelines for suture bites appears acceptable during microarterial anastomoses if the goal is vessel patency. However, we believe clinical competence involves the ability to place small, even bites consistently and uniformly. During microsurgical training, the occasional large bite need not be replaced; however, the trainee should be encouraged to take standard bites.

Retinal vasculature segmentation and measurement framework for color fundus and SLO images

The change in vascular geometry is an indicator of various health issues linked with vision and cardiovascular risk factors. Early detection and diagnosis of these changes can help patients to select an appropriate treatment option when the disease is in its primary phase. Automatic segmentation and quantification of these vessels would decrease the cost and eliminate inconsistency related to manual grading. However, automatic detection of the vessels is challenging in the presence of retinal pathologies and non-uniform illumination, two common occurrences in clinical settings. This paper presents a novel framework to address the issue of retinal blood vessel detection and width measurement under these challenging circumstances and also on two different imaging modalities: color fundus imaging and Scanning Laser Ophthalmoscopy (SLO). In this framework, initially, vessel enhancement is done using linear recursive filtering. Then, a unique combination of morphological operations, background estimation, and iterative thresholding are applied to segment the blood vessels. Further, vessel diameter is estimated in two steps: firstly, vessel centerlines are extracted using the graph-based algorithm. Then, vessel edges are localized from the image profiles, by utilizing spline fitting to obtain vascular orientations and then finding the zero-crossings. Extensive experiments have been carried out on several publicly accessible datasets for vessel segmentation and diameter measurement, i.e., DRIVE, STARE, IOSTAR, RC-SLO and REVIEW dataset. Results demonstrate the competitive and comparable performance than earlier methods. The encouraging quantitative and visual performance of the proposed framework makes it an important component of a decision support system for retinal images.

Retina blood vessel extraction based on kirsch’s template method

&lt;p class="IJASEITAbtract"&gt;Analysis on the retina blood vessels from fundus images have been widely used in the medical community to detect the disorder condition in the blood vessels. An automated tracing of retina blood vessel can help to provide valuable computer-assisted diagnosis for the ophthalmic disorders. Thus, it helps to reduce the time for the ophthalmologist to analyses and diagnose the result of the fundus image of patient. The purpose of this research is to build an algorithm to trace the retina blood vessels. The method to be used in this research consist of two parts which are the pre-processing part and the feature extraction by using the Kirsch’s template. Combining the pre-processing at the early stage and feature extraction at the next stage is applied to extract the edges of the blood vessels. The proposed algorithm was verified by using two online databases, DRIVE and HRF to validate the performance measures. Hence, proposed method is capable to extract the retina blood vessel and give the accuracy of 0.7917, the sensitivity of 0.9077 and the specificity of 0.7215. In conclusion, the extraction of the blood vessels is highly recommended as the early screening stage for the eye diseases beneficially.&lt;/p&gt;