Vessel Segmentation Research Articles

High-risk plaque features and perivascular inflammation.

The association between high-risk plaque (HRP) on coronary computed tomography angiography (CTA) and the level of perivascular inflammation has not been fully investigated. Patients who underwent both CTA and optical coherence tomography (OCT) were included. The level of perivascular inflammation was assessed by pericoronary adipose tissue (PCAT) attenuation at two levels: lesion-specific and the proximal segment of the culprit vessel. HRP features included positive remodeling (PR), low-attenuation plaque (LAP), napkin-ring sign (NRS), and spotty calcification (SC). OCT features of plaque vulnerability were evaluated in culprit vessels. A total of 1360 lesions (413 culprit lesions and 947 non-culprit lesions) from 413 patients were evaluated. Lesion-specific PCAT attenuation was higher in lesions with any HRP feature except SC (present vs. absent: PR -71.3​±​10.1 vs. -74.1​±​11.7, P​<​0.001; LAP -71.7​±​9.9 vs. -73.0​±​11.4, P​=​0.025; NRS -70.3​±​9.6 vs. -72.9​±​11.1, P​=​0.001; and SC -71.9​±​9.9 vs. -73.0​±​11.5, P​=​0.082). After adjusting for confounders, only PR was associated with higher lesion-specific PCAT attenuation. The number of lesions with PR significantly correlated with higher levels of perivascular inflammation measured by culprit vessel PCAT attenuation. The number of lesions with PR was associated with higher lipid index and macrophage grade at culprit vessels. Among 4 HRP features, only PR was significantly associated with higher lesion-specific PCAT attenuation. The number of plaques with PR correlated with the level of perivascular inflammation and vulnerability. clinicaltrials.gov Identifier: NCT04523194.

PlaqueViT: a vision transformer model for fully automatic vessel and plaque segmentation in coronary computed tomography angiography.

To develop and evaluate a deep learning model for segmentation of the coronary artery vessels and coronary plaques in coronary computed tomography angiography (CCTA). CCTA image data from the Swedish CardioPulmonary BioImage Study (SCAPIS) was used for model development (n = 463 subjects) and testing (n = 123) and for an interobserver study (n = 65). A dataset from Linköping University Hospital (n = 28) was used for external validation. The model's ability to detect coronary artery disease (CAD) was tested in a separate SCAPIS dataset (n = 684). A deep ensemble (k = 6) of a customized 3D vision transformer model was used for voxelwise classification. The Dice coefficient, the average surface distance, Pearson's correlation coefficient, analysis of segmented volumes by intraclass correlation coefficient (ICC), and agreement (sensitivity and specificity) were used to analyze model performance. PlaqueViT segmented coronary plaques with a Dice coefficient = 0.55, an average surface distance = 0.98 mm and ICC = 0.93 versus an expert reader. In the interobserver study, PlaqueViT performed as well as the expert reader (Dice coefficient = 0.51 and 0.50, average surface distance = 1.31 and 1.15 mm, ICC = 0.97 and 0.98, respectively). PlaqueViT achieved 88% agreement (sensitivity 97%, specificity 76%) in detecting any coronary plaque in the test dataset (n = 123) and 89% agreement (sensitivity 95%, specificity 83%) in the CAD detection dataset (n = 684). We developed a deep learning model for fully automatic plaque detection and segmentation that identifies and delineates coronary plaques and the arterial lumen with similar performance as an experienced reader. Question A tool for fully automatic and voxelwise segmentation of coronary plaques in coronary CTA (CCTA) is important for both clinical and research usage of the CCTA examination. Findings Segmentation of coronary artery plaques by PlaqueViT was comparable to an expert reader's performance. Clinical relevance This novel, fully automatic deep learning model for voxelwise segmentation of coronary plaques in CCTA is highly relevant for large population studies such as the Swedish CardioPulmonary BioImage Study.

WLKA-RVS: a retinal vessel segmentation method using weighted large kernel attention

WLKA-RVS: a retinal vessel segmentation method using weighted large kernel attention

Analisis Perbedaan Citra SPECT Non-AC dan CTAC pada Pemeriksaan Scintigrafi Perfusi Miokard

Background: Myocardial Perfusion Scan displays SPECT images, which is a non invasive imaging method for a quantitative view of myocardial ischemia which can’t be done with CT myocardial perfusion. The principle of myocardial perfusion scan is that the myocardial absorption of radiopharmaceuticals is proportional to coronary heart blood flow to the myocardium. The main problem with SPECT myocardial perfusion scan imaging is photon attenuation caused by soft tissue around the heart which causes inaccurate information and attenuation artifacts. One method to reduce attenuation artifacts is hybrid CT Scan imaging with SPECT tools. CT Scan images are used to correct attenuation in SPECT images. The purpose of this research is to analyze the differences between non-AC and CTAC SPECT images which will have an impact on myocardium perfusion values on myocardial perfusion scans.Methods: This research is a quantitative analysis with a direct observation approach. The research was undertaken at Nuclear Medicine and Molecular Theranostics Installation of Dr. Hasan Sadikin General Hospital in Bandung, starting November 2023 using a SPECT CT GE NM/CT 860 gamma camera. A total of 28 patients were examined for myocardial perfusion scans. Data processing is conducted by comparing the perfusion values of myocardium on non-AC and CTAC SPECT images for 28 samples by conducting hypothesis testing using paired test sample T-Test.Result: Based on the research results, non-AC, and CTAC SPECT image data showed a normal distribution using the Shapiro-Wilk test, allowing the analysis of differences using the Paired-Samples T-test. In general, the analysis results indicate a significant difference between non-AC and CTAC SPECT images, as the sig value is less than 0.05 in the RCA and LCx vascular segments, as well as in the septal, inferior, lateral, and apical wall segments. However, no significant differences were found in the LAD vascular segment and the anterior wall segment, as the sig value is greater than 0.05.Conclusions: There are differences between non-AC and CTAC SPECT images in the RCA and LCx blood vessel segments, as well as in the septal, inferior, lateral, and apical wall segments. However, no differences are observed in the LAD blood vessel segment and the anterior wall segment.

Quantifying the tumour vasculature environment from CD-31 immunohistochemistry images of breast cancer using deep learning based semantic segmentation

BackgroundTumour vascular density assessed from CD-31 immunohistochemistry (IHC) images has previously been shown to have prognostic value in breast cancer. Current methods to measure vascular density, however, are time-consuming, suffer from high inter-observer variability and are limited in describing the complex tumour vasculature morphometry.MethodsWe propose a method for automatically measuring a range of vascular parameters from CD-31 IHC images, which together provide a detailed description of the vasculature morphology. We first used a U-Net based convolutional neural network, trained and validated using 36 partially annotated whole slide images from 27 patients, to segment vessel structures and tumour regions from which the measurements are taken. The model also segments the vascular smooth muscle, benign epithelium, adipose tissue, stroma, lymphocyte clusters, nerves and CD-31 positive leukocytes, and we applied it to an additional 21 images from 15 patients. Using these segmentations, we investigated the relationship between the various tissue types and the vasculature and studied the relationship of various vascular parameters with clinical parameters. We also performed a 3D histology analysis on a separate tumour sample as a proof of principle, providing a more comprehensive visualization of vasculature morphology compared to the standard 2D cross-section of a tissue sample.ResultsUsing two-way cross-validation, we show that vessels were accurately segmented, with Dice scores of 0.875 and 0.856, and were accurately identified, with F1 scores of 0.777 and 0.748. All vascular parameters exhibit strong (r>0.70.7$$\\end{document}]]>) and significant (p<0.001) correlations with measurements taken from the manual ground truth vessel segmentations. A significant relationship between the major/minor axis ratio, a measure of elongation, and the tumour grade was found.ConclusionOur proposed method shows promise as a tool for studying the tumour vasculature and its relationship with surrounding cells and tissue types. Furthermore, the correlation with tumour grade highlights the clinical relevance of our approach. These findings suggest that our method could have substantial implications for improving prognostic assessments and personalizing therapeutic strategies in breast cancer treatment.

Multi-scale dual attention embedded U-shaped network for accurate segmentation of coronary vessels in digital subtraction angiography.

Most attention-based networks fall short in effectively integrating spatial and channel-wise information across different scales, which results in suboptimal performance for segmenting coronary vessels in x-ray digital subtraction angiography (DSA) images. This limitation becomes particularly evident when attempting to identify tinysub-branches. To address this limitation, a multi-scale dual attention embedded network (named MDA-Net) is proposed to consolidate contextual spatial and channel information across contiguous levels and scales. MDA-Net employs five cascaded double-convolution blocks within its encoder to adeptly extract multi-scale features. It incorporates skip connections that facilitate the retention of low-level feature details throughout the decoding phase, thereby enhancing the reconstruction of detailed image information. Furthermore, MDA modules, which take in features from neighboring scales and hierarchical levels, are tasked with discerning subtle distinctions between foreground elements, such as coronary vessels of diverse morphologies and dimensions, and the complex background, which includes structures like catheters or other tissues with analogous intensities. To sharpen the segmentation accuracy, the network utilizes a composite loss function that integrates intersection over union (IoU) loss with binary cross-entropy loss, ensuring the precision of the segmentation outcomes and maintaining an equilibrium between positive and negativeclassifications. Experimental results demonstrate that MDA-Net not only performs more robustly and effectively on DSA images under various image conditions, but also achieves significant advantages over state-of-the-art methods, achieving the optimal scores in terms of IoU, Dice, accuracy, and Hausdorff distance 95%. MDA-Net has high robustness for coronary vessels segmentation, providing an active strategy for early diagnosis of cardiovascular diseases. The code is publicly available at https://github.com/30410B/MDA-Net.git.

Large blood vessel segmentation in quantitative DCE-MRI of brain tumors: A Swin UNETR approach.

Brain tumor growth is associated with angiogenesis, wherein the density of newly developed blood vessels indicates tumor progression and correlates with the tumor grade. Quantitative dynamic contrast enhanced-magnetic resonance imaging (DCE-MRI) has shown potential in brain tumor grading and treatment response assessment. Segmentation of large-blood-vessels is crucial for automatic and accurate tumor grading using quantitative DCE-MRI. Traditional manual and semi-manual rule-based large-blood-vessel segmentation methods are time-intensive and prone to errors. This study proposes a novel deep learning-based technique for automatic large-blood-vessel segmentation using Swin UNETR architectures and comparing it with U-Net and Attention U-Net architectures. The study employed MRI data from 187 brain tumor patients, with training, validation, and testing datasets sourced from two centers, two vendors, and two field-strength magnetic resonance scanners. To test the generalizability of the developed model, testing was also carried out on different brain tumor types, including lymphoma and metastasis. Performance evaluation demonstrated that Swin UNETR outperformed other models in segmenting large-blood-vessel regions (achieving Dice scores of 0.979, and 0.973 on training and validation sets, respectively, with test set performance ranging from 0.835 to 0.982). Moreover, most quantitative parameters showed significant differences (p < 0.05) between with and without large-blood-vessel. After large-blood-vessel removal, using both ground truth and predicted masks, the values of parameters in non-vascular tumoral regions were statistically similar (p > 0.05). The proposed approach has potential applications in improving the accuracy of automatic grading of tumors as well as in treatment planning.

Dual-stream disentangled model for microvascular extraction in five datasets from multiple OCTA instruments

IntroductionOptical Coherence Tomography Angiography (OCTA) is a cutting-edge imaging technique that captures retinal capillaries at micrometer resolution using optical instrument. Accurate segmentation of retinal vasculature is essential for eye related diseases measurement and diagnosis. However, noise and artifacts from different imaging instruments can interfere with segmentation, and most existing deep learning models struggle with segmenting small vessels and capturing low-dimensional structural information. These challenges typically results in less precise segmentation performance.MethodsTherefore, we propose a novel and robust Dual-stream Disentangled Network (D2Net) for retinal OCTA microvascular segmentation. Specifically, the D2Net includes a dual-stream encoder that separately learns image artifacts and latent vascular features. By introducing vascular structure as a prior constraint and constructing auxiliary information, the network achieves disentangled representation learning, effectively minimizing the interference of noise and artifacts. The introduced vascular structure prior includes low-dimensional neighborhood energy from the Distance Correlation Energy (DCE) module, which helps to better perceive the structural information of continuous vessels.Results and discussionTo precisely evaluate our method on small vessels, we delicately establish OCTA microvascular labels by performing comprehensive and detailed annotations on the FOCA dataset, which includes data collected from different instruments, and evaluated the proposed D2Net effectively mitigates the challenges of microvasculature region recognition caused by noise and artifacts. The method achieves more refined segmentation performance. In addition, we validated the performance of D2Net on four OCTA datasets (OCTA-500, ROSE-O, ROSE-Z, and ROSE-H) acquired using different instruments, demonstrating its robustness and generalization capabilities in retinal vessel segmentation compared to other state-of-the-art methods.

A novel multi-scale and fine-grained network for large choroidal vessels segmentation in OCT

Accurate segmentation of large choroidal vessels using optical coherence tomography (OCT) images enables unprecedented quantitative analysis to understand choroidal diseases. In this paper, we propose a novel multi-scale and fine-grained network called MFGNet. Since choroidal vessels are small targets, long-range dependencies need to be considered, therefore, we developed a two-branch fine-grained feature extraction module that can mix the long-range information extracted by TransFormer with the local information extracted by convolution in parallel, introducing information exchange between the two branches. To address the problem of low contrast and blurred boundaries of choroidal vessels in OCT images, we developed a large kernel and multi-scale attention module, which can improve the features of the target area through multi-scale convolution kernels, channel mixing and feature refinement. We quantitatively evaluated the MFGNet on 800 OCT images with large choroidal vessels manually annotated. The experimental results show that the proposed method has the best performance compared to the most advanced segmentation networks currently available. It is noteworthy that the large choroidal vessels were reconstructed in three dimensions (3D) based on the segmentation results and several 3D morphological parameters were calculated. The statistical analysis of these parameters revealed significant differences between the healthy control group and the high myopia group, thereby confirming the value of the proposed work in facilitating subsequent understanding of the disease and clinical decision-making.

Multi scale multi attention network for blood vessel segmentation in fundus images

Precise segmentation of retinal vasculature is crucial for the early detection, diagnosis, and treatment of vision-threatening ailments. However, this task is challenging due to limited contextual information, variations in vessel thicknesses, the complexity of vessel structures, and the potential for confusion with lesions. In this paper, we introduce a novel approach, the MSMA Net model, which overcomes these challenges by replacing traditional convolution blocks and skip connections with an improved multi-scale squeeze and excitation block (MSSE Block) and Bottleneck residual paths (B-Res paths) with spatial attention blocks (SAB). Our experimental findings on publicly available datasets of fundus images, specifically DRIVE, STARE, CHASE_DB1, HRF and DR HAGIS consistently demonstrate that our approach outperforms other segmentation techniques, achieving higher accuracy, sensitivity, Dice score, and area under the receiver operator characteristic (AUC) in the segmentation of blood vessels with different thicknesses, even in situations involving diverse contextual information, the presence of coexisting lesions, and intricate vessel morphologies.

LASF: a local adaptive segmentation framework for coronary angiogram segments.

Coronary artery disease (CAD) remains the leading cause of death globally, highlighting the critical need for accurate diagnostic tools in medical imaging. Traditional segmentation methods for coronary angiograms often struggle with vessel discontinuity and inaccuracies, impeding effective diagnosis and treatment planning. To address these challenges, we developed the Local Adaptive Segmentation Framework (LASF), enhancing the YOLOv8 architecture with dilation and erosion algorithms to improve the continuity and precision of vascular image segmentation. We further enriched the ARCADE dataset by meticulously annotating both proximal and distal vascular segments, thus broadening the dataset's applicability for training robust segmentation models. Our comparative analyses reveal that LASF outperforms well-known models such as UNet and DeepLabV3Plus, demonstrating superior metrics in precision, recall, and F1-score across various testing scenarios. These enhancements ensure more reliable and accurate segmentation, critical for clinical applications. LASF represents a significant advancement in the segmentation of vascular images within coronary angiograms. By effectively addressing the common issues of vessel discontinuity and segmentation accuracy, LASF stands to improve the clinical management of CAD, offering a promising tool for enhancing diagnostic accuracy and patient outcomes in medical settings.

COMPUTED TOMOGRAPHIC ANGIOGRAPHY DATA SET CONTAINING ABDOMINAL AORTIC SEGMENTATION

BACKGROUND: Artificial Intelligence (AI) is widely actively in radiology. The effectiveness of AI algorithms essentially depends on the availability of relevant and representative training and test datasets. Currently, such datasets are highly demanded in open access, particularly CT angiographic (CTA) studies of the abdominal aorta containing not only pathology classification but also vessel segmentation. The disadvantages of existing solutions include small sample volume, narrow specialization, and disparate data preparation methodology. AIM – Design of a CTA dataset containing abdominal aortic segmentation for cases of normal, dilatation, thrombosis, and calcinosis. METHODS: In accordance with the methodology of AI-algorithms testing, the terms of reference for datasets preparation were developed, the necessary sample volume was calculated and approval of the independent ethical committee was obtained. Previously designed original algorithm of semi-automatic segmentation of abdominal aorta on CTA-studies using open access software Slicer 3D was used. Inclusion criteria: CTA studies, or abdominal CT studies with contrast enhancement; presence of arterial phase of the scan; slice thickness not more than 3 mm. Exclusion criteria: presence of any foreign objects in the aortic lumen, aortic dissection. The algorithm was tested on patient data obtained from the Unified Radiologic Information System. Expert evaluation of the compliance of the obtained results with the formed requirements, as well as the assessment of time costs when using the developed segmentation algorithm was carried out. RESULTS: The calculated sample size was 100 CTA studies with a slice thickness ≤ 1.2 mm. Population data: the number of unique patients was 100, the proportion of female patients was 51%; the median age was 62 years ranged from 18 to 84 years. The proportion of pathologic studies was 61%, of which (including combined pathology): 60 cases contained calcification, both by 18 cases with aortic dilatation and thrombosed lumen. Different non-overlapping masks were used for each object. The dataset is presented in the public domain. The average processing time is 0.8 hours per 100 CT-images. CONCLUSION: The public dataset containing 100 CTA studies of segmented abdominal aorta for normal, dilated, thrombotic and calcified cases was created. Results are valuable for AI-algorithms design and anthropomorphic 3D-modeling.

DAU-Net: a novel U-Net with dual attention for retinal vessel segmentation

In fundus images, precisely segmenting retinal blood vessels is important for diagnosing eye-related conditions, such as diabetic retinopathy and hypertensive retinopathy or other eye-related disorders. In this work, we propose an enhanced U-shaped network with dual-attention, named DAU-Net, divided into encoder and decoder parts. Wherein, we replace the traditional convolutional layers with ConvNeXt Block and SnakeConv Block to strengthen its recognition ability for different forms of blood vessels while lightweight the model. Additionally, we designed two efficient attention modules, namely Local-Global Attention (LGA) and Cross-Fusion Attention (CFA). Specifically, LGA conducts attention calculations on the features extracted by the encoder to accentuate vessel-related characteristics while suppressing irrelevant background information; CFA addresses potential information loss during feature extraction by globally modeling pixel interactions between encoder and decoder features. Comprehensive experiments in terms of public datasets DRIVE, CHASE_DB1, and STARE demonstrate that DAU-Net obtains excellent segmentation results on all three datasets. The results show an AUC of 0.9818, ACC of 0.8299, and F1 score of 0.9585 on DRIVE; 0.9894, 0.8499, and 0.9700 on CHASE_DB1; and 0.9908, 0.8620, and 0.9712 on STARE, respectively. These results strongly demonstrate the effectiveness of DAU-Net in retinal vessel segmentation, highlighting its potential for practical clinical use.

Predicting branch retinal vein occlusion development using multimodal deep learning and pre-onset fundus hemisection images

Branch retinal vein occlusion (BRVO) is a leading cause of visual impairment in working-age individuals, though predicting its occurrence from retinal vascular features alone remains challenging. We developed a deep learning model to predict BRVO based on pre-onset, metadata-matched fundus hemisection images. This retrospective cohort study included patients diagnosed with unilateral BRVO from two Korean tertiary centers (2005–2023), using hemisection fundus images from 27 BRVO-affected eyes paired with 81 unaffected hemisections (27 counter and 54 contralateral) for training. A U-net model segmented retinal optic discs and blood vessels (BVs), dividing them into upper and lower halves labeled for BRVO occurrence. Both unimodal models (using either fundus or BV images) and a BV-enhanced multimodal model were constructed to predict future BRVO. The multimodal model outperformed the unimodal models achieving an area under the receiver operating characteristic curve of 0.76 (95% confidence interval [CI], 0.66–0.83) and accuracy of 68.5% (95% CI 58.9–77.1%), with predictions focusing on arteriovenous crossing regions in the retinal vascular arcade. These findings demonstrate the potential of the BV-enhanced multimodal approach for BRVO prediction and highlight the need for larger, multicenter datasets to improve its clinical utility and predictive accuracy.

COph100: A comprehensive fundus image registration dataset from infants constituting the “RIDIRP” database

Retinal image registration is vital for diagnostic therapeutic applications within the field of ophthalmology. Existing public datasets, focusing on adult retinal pathologies with high-quality images, have limited number of image pairs and neglect clinical challenges. To address this gap, we introduce COph100, a novel and challenging dataset known as the Comprehensive Ophthalmology Retinal Image Registration dataset for infants with a wide range of image quality issues constituting the public “RIDIRP” database. COph100 consists of 100 eyes, each with 2 to 9 examination sessions, amounting to a total of 491 image pairs carefully selected from the publicly available dataset. We manually labeled the corresponding ground truth image points and provided automatic vessel segmentation masks for each image. We have assessed COph100 in terms of image quality and registration outcomes using state-of-the-art algorithms. This resource enables a robust comparison of retinal registration methodologies and aids in the analysis of disease progression in infants, thereby deepening our understanding of pediatric ophthalmic conditions.

Unsupervised domain adaptation teacher–student network for retinal vessel segmentation via full-resolution refined model

Retinal blood vessels are the only blood vessels in the human body that can be observed non-invasively. Changes in vessel morphology are closely associated with hypertension, diabetes, cardiovascular disease and other systemic diseases, and computers can help doctors identify these changes by automatically segmenting blood vessels in fundus images. If we train a highly accurate segmentation model on one dataset (source domain) and apply it to another dataset (target domain) with a different data distribution, the segmentation accuracy will drop sharply, which is called the domain shift problem. This paper proposes a novel unsupervised domain adaptation method to address this problem. It uses a teacher–student framework to generate pseudo labels for the target domain image, and trains the student network with a combination of source domain loss and domain adaptation loss; finally, the weights of the teacher network are updated from the exponential moving average of the student network and used for the target domain segmentation. We reconstructed the encoder and decoder of the network into a full-resolution refined model by computing the training loss at multiple semantic levels and multiple label resolutions. We validated our method on two publicly available datasets DRIVE and STARE. From STARE to DRIVE, the accuracy, sensitivity, and specificity are 0.9633, 0.8616,and 0.9733, respectively. From DRIVE to STARE, the accuracy, sensitivity, and specificity are 0.9687, 0.8470, and 0.9785, respectively. Our method outperforms most state-of-the-art unsupervised methods. Compared with domain adaptation methods, our method also has the best F1 score (0.8053) from STARE to DRIVE and a competitive F1 score (0.8001) from DRIVE to STARE.

LRNet: Link Residual Neural Network for Blood Vessel Segmentation in OCTA Images.

Optical coherence tomography angiography (OCTA) is an emerging, non-invasive technique increasingly utilized for retinal vasculature imaging. Analysis of OCTA images can effectively diagnose retinal diseases, unfortunately, complex vascular structures within OCTA images possess significant challenges for automated segmentation. A novel, fully convolutional dense connected residual network is proposed to effectively segment the vascular regions within OCTA images. Firstly, a dual-branch structure Recurrent Residual Convolutional Neural Network (RRCNN) block is constructed utilizing RecurrentBlock and convolutional operations. Subsequently, the ResConvNeXt V2 Block is built as the backbone structure of the network. The output from the ResConvNeXt V2 Block is then fed into the side branch and the next ResConvNeXt V2 Block. Within the side branch, the Group Receptive Field Block (GRFB) processes the results from the previous and current layers. Ultimately, the side branch results are added to the backbone network outputs to produce the final segmentation. The model achieves superior performance. Experiments were conducted on the ROSSA and OCTA-500 datasets, yielding Dice scores of 91.88%, 91.72%, and 89.18% for the respective datasets, and accuracies of 98.31%, 99.02%, and 98.02%.

Segmenting Small Blood Vessels in MRA/MRI: A Machine Learning Approach with Bilateral Filtering Integration

The adoption of artificial intelligence (AI) in various sectors, including healthcare, has gained significant popularity due to its potential to improve services. In the medical field, misdiagnosis has been a major problem, leading to increased mortality rates. Accurate diagnosis is crucial for effective treatment and management of diseases. This research aims to develop a machine-learning model for segmenting small blood vessels in magnetic resonance angiography (MRA) and magnetic resonance imaging (MRI) datasets using bilateral filtering. The research identifies the limitations of existing machine learning models in blood vessel segmentation, particularly the loss of important edge information due to convolutions that blur images. To address this issue, a non-linear bilateral filter is introduced to enhance the segmentation of blood vessels in MRI images. The proposed framework aims to improve the accuracy of the segmentation algorithm by reducing image blurring and noise through bilateral filtering. The objectives of this research include training and testing a machine-learning prototype using bilateral filtering, exploring the weaknesses of existing models in blood vessel segmentation, and developing a machine-learning model specifically designed for segmenting small blood vessels using bilateral filtering. Various studies have proposed machine learning algorithms, such as convolutional neural networks, for blood vessel segmentation. The review emphasizes the importance of bilateral filtering in improving classification accuracy by reducing image blurring. In conclusion, this research aims to contribute to the field of medical image analysis by developing a framework that utilizes bilateral filtering to enhance the segmentation of small blood vessels in MRA and MRI datasets. The proposed machine learning model has the potential to improve the accuracy of blood vessel segmentation, enabling more accurate diagnoses and reducing misdiagnosis-related mortality rates.

A Novel Ensemble Meta-Model for Enhanced Retinal Blood Vessel Segmentation Using Deep Learning Architectures.

Background: Retinal blood vessel segmentation plays an important role in diagnosing retinal diseases such as diabetic retinopathy, glaucoma, and hypertensive retinopathy. Accurate segmentation of blood vessels in retinal images presents a challenging task due to noise, low contrast, and the complex morphology of blood vessel structures. Methods: In this study, we propose a novel ensemble learning framework combining four deep learning architectures: U-Net, ResNet50, U-Net with a ResNet50 backbone, and U-Net with a transformer block. Each architecture is customized to enhance feature extraction and segmentation performance. The models are trained on the DRIVE and STARE datasets to improve the degree of generalization and evaluated using the performance metric accuracy, F1-Score, sensitivity, specificity, and AUC. Results: The ensemble meta-model integrates predictions from these architectures using a stacking approach, achieving state-of-the-art performance with an accuracy of 0.9778, an AUC of 0.9912, and an F1-Score of 0.8231. These results demonstrate the performance of the proposed technique in identifying thin retinal blood vessels. Conclusions: A comparative analysis using qualitative and quantitative results with individual models highlights the robustness of the ensemble framework, especially under conditions of noise and poor visibility.

A Robust Blood Vessel Segmentation Technique for Angiographic Images Employing Multi-Scale Filtering Approach.

Background: This study focuses on the critical task of blood vessel segmentation in medical image analysis, essential for diagnosing cardiovascular diseases and enabling effective treatment planning. Although deep learning architectures often produce very high segmentation results in medical images, coronary computed tomography angiography (CTA) images are more challenging than invasive coronary angiography (ICA) images due to noise and the complexity of vessel structures. Methods: Classical architectures for medical images, such as U-Net, achieve only moderate accuracy, with an average Dice score of 0.722. Results: This study introduces Morpho-U-Net, an enhanced U-Net architecture that integrates advanced morphological operations, including Gaussian blurring, thresholding, and morphological opening/closing, to improve vascular integrity, reduce noise, and achieve a higher Dice score of 0.9108, a precision of 0.9341, and a recall of 0.8872. These enhancements demonstrate superior robustness to noise and intricate vessel geometries. Conclusions: This pre-processing filter effectively reduces noise by grouping neighboring pixels with similar intensity values, allowing the model to focus on relevant anatomical structures, thus outperforming traditional methods in handling the challenges posed by CTA images.