Vascular diseases, such as carotid stenosis (narrowing of the carotid arteries, which connect the heart to the brain, caused by the accumulation of fatty atheroma plaques), cerebrovascular accidents (CVA) (sudden interruption of blood flow to the brain), atherosclerosis (accumulation of atheroma plaques in the arteries, leading to arterial hardening and narrowing), deep vein thrombosis (formation of blood clots in deep veins), and cervical arterial dissections (tears in the inner layer of the neck arteries), represent a significant challenge to global public health due to their high prevalence and clinical impact. Vessels
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Home / Publications / Publication
AUTOMATIC SEGMENTATION OF BLOOD VESSELS IN CAROTID ULTRASOUND IMAGES
Publication type: Article Summary
Original title: Vessel Detection In Carotid Ultrasound Images Using Artificial Neural Networks
Article publication date: July 2018
Source: Proceedings of the 6th International Conference Integrity, Reliability and Failure
Authors: Catarina F. Castro, Carlos C. António & Luísa C. Sousa
What is the goal, target audience, and areas of digital health it addresses?
The study aims to automatically identify blood vessels and segment carotid ultrasound images, improving diagnostic accuracy and clinical efficiency. The target audience includes healthcare professionals, such as radiologists and angiologists, as well as healthcare institutions and researchers in the fields of Artificial Intelligence (AI) and medical image processing. The study falls within the areas of digital health, with a focus on the application of AI and diagnostic support technologies.
What is the context?
Vascular diseases, such as carotid stenosis (narrowing of the carotid arteries, which connect the heart to the brain, caused by the accumulation of fatty atheroma plaques), cerebrovascular accidents (CVA) (sudden interruption of blood flow to the brain), atherosclerosis (accumulation of atheroma plaques in the arteries, leading to arterial hardening and narrowing), deep vein thrombosis (formation of blood clots in deep veins), and cervical arterial dissections (tears in the inner layer of the neck arteries), represent a significant challenge to global public health due to their high prevalence and clinical impact. Carotid stenosis is a major risk factor for CVA, affecting millions of people annually. Atherosclerosis, the leading cause of cardiovascular complications, is increasing with population ageing and risk factors such as obesity, sedentary lifestyles, and hypertension. Deep vein thrombosis, which occurs in 1 to 2 individuals per 1000 annually, can lead to pulmonary embolism (blockage of a pulmonary artery by an embolus), a severe and often fatal complication. Although less common, cervical arterial dissections are a significant cause of CVA in young adults and are frequently underdiagnosed until severe complications arise.
These conditions highlight the urgent need for advanced diagnostic tools that enable early detection and accurate assessment of these pathologies, contributing to improved clinical management and reduced complications.
What are the current approaches?
Current approaches for the diagnosis and monitoring of vascular diseases integrate advanced imaging techniques, computational methods, and clinical tools to enhance accuracy and efficiency in diagnostic outcomes. Doppler ultrasound is widely used due to its non-invasive nature, speed, and low cost, proving effective in assessing blood flow and detecting arterial obstructions. More sophisticated methods, such as computed tomography angiography and magnetic resonance angiography, provide high-resolution imaging that facilitates the detection of atheroma plaques, arterial dissections, and vascular occlusions (blockages in blood vessels). Additionally, transcranial Doppler ultrasound plays a crucial role in real-time monitoring of cerebral blood flow, proving useful in assessing conditions such as intracranial stenosis (narrowing of arteries within the skull) and other cerebrovascular disorders.
Despite their clinical utility, these techniques present limitations, including operator-dependent settings, image irregularities, and challenges in distinguishing vascular from non-vascular structures in transverse images. These factors can compromise diagnostic accuracy, particularly in cases with image artifacts or noise. Such limitations highlight the urgent need for automated, more robust, and precise solutions that can minimize variability and errors associated with manual analysis, ensuring greater consistency and accuracy in the diagnosis and monitoring of vascular diseases.
What does innovation consist of?
The innovation of this study lies in the development of an advanced methodology for the automatic segmentation of blood vessels in transverse ultrasound images of the carotid artery. The proposed approach structured into three main stages: preprocessing, geometric modelling, and classification based on artificial neural networks.
In the preprocessing stage, the region of interest in the images is binarized, removing noise smaller than 1% of the total pixels. The identification of the vascular lumen (the internal space of blood vessels) is optimized by maximizing three parameters: the circularity index, which evaluates the circular shape of structures; the irregularity index, which penalizes irregular contours; and the centrality index, which prioritizes structures positioned at the centre of the image. This combination enables a robust segmentation, accurately distinguishing the lumen from the vessel wall and assigning greyscale levels to highlight the regions of interest.
In the geometric modelling stage, Bézier curves used to smooth vascular contours. The contour pixels are organized into upper and lower subsets based on their vertical orientation, allowing for the creation of two Bézier curves that then merged to form a continuous and precise outline of the vessel walls.
In the classification stage, AI plays a key role. An advanced neural network is applied to analyse candidate regions for blood vessels, determining with high accuracy the presence or absence of vascular structures. This network was trained and tested using real ultrasound images of varying quality and acquisition settings, ensuring the model’s robustness and generalization capability across different clinical scenarios. During training, dynamic adjustments to the neural network’s internal parameters enhanced precision and minimized classification errors. Additionally, data augmentation techniques, such as horizontal image flipping, were employed to expand the dataset, significantly increasing the volume of information available for model training and validation.
What are the main results? What is the future of this approach?
Although this study does not present detailed quantitative data, the results obtained from training the model with real ultrasound images demonstrate significant advances in the accuracy and efficiency of vascular disease diagnosis. The integration of artificial neural networks in this approach enables the automated classification of candidate regions, ensuring a reliable distinction between blood vessels and adjacent structures, even in suboptimal image quality or challenging clinical conditions. Additionally, the use of Bézier curves in geometric modelling allows for the generation of precise and continuous contours of vascular walls, enhancing the detection and delineation of vascular structures. This balance between image processing techniques and AI stands out as a robust and adaptable approach that overcomes the limitations of traditional methods, such as operator-dependent settings, image noise interference, and the difficulty in distinguishing vascular from non-vascular structures.
The future of this approach is promising for transforming vascular diagnostics. Expanding the training dataset could improve the model’s generalization, making it more robust and effective across different populations and clinical settings. Its implementation in hospital software and portable ultrasound devices could enable faster, more accessible, and real-time diagnostics, particularly in regions with limited medical resources. Furthermore, this methodology can be adapted to analyse other vessels beyond the carotid artery, such as coronary and peripheral arteries, expanding its impact on cardiovascular pathologies. When combined with other imaging modalities, such as computed tomography and magnetic resonance imaging, this approach could provide more detailed and comprehensive diagnoses. Additionally, integrating clinical data, biomarkers, and demographic information allows for personalized diagnostics, aligning with the trend toward precision medicine. This innovation optimizes clinical workflows and reinforces the relevance of AI in digital health.
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Home / Publications / Publication
AUTOMATIC SEGMENTATION OF BLOOD VESSELS IN CAROTID ULTRASOUND IMAGES
Publication type: Article Summary
Original title: Vessel Detection In Carotid Ultrasound Images Using Artificial Neural Networks
Article publication date: July 2018
Source: Proceedings of the 6th International Conference Integrity, Reliability and Failure
Authors: Catarina F. Castro, Carlos C. António & Luísa C. Sousa
What is the goal, target audience, and areas of digital health it addresses?
The study aims to automatically identify blood vessels and segment carotid ultrasound images, improving diagnostic accuracy and clinical efficiency. The target audience includes healthcare professionals, such as radiologists and angiologists, as well as healthcare institutions and researchers in the fields of Artificial Intelligence (AI) and medical image processing. The study falls within the areas of digital health, with a focus on the application of AI and diagnostic support technologies.
What is the context?
Vascular diseases, such as carotid stenosis (narrowing of the carotid arteries, which connect the heart to the brain, caused by the accumulation of fatty atheroma plaques), cerebrovascular accidents (CVA) (sudden interruption of blood flow to the brain), atherosclerosis (accumulation of atheroma plaques in the arteries, leading to arterial hardening and narrowing), deep vein thrombosis (formation of blood clots in deep veins), and cervical arterial dissections (tears in the inner layer of the neck arteries), represent a significant challenge to global public health due to their high prevalence and clinical impact. Carotid stenosis is a major risk factor for CVA, affecting millions of people annually. Atherosclerosis, the leading cause of cardiovascular complications, is increasing with population ageing and risk factors such as obesity, sedentary lifestyles, and hypertension. Deep vein thrombosis, which occurs in 1 to 2 individuals per 1000 annually, can lead to pulmonary embolism (blockage of a pulmonary artery by an embolus), a severe and often fatal complication. Although less common, cervical arterial dissections are a significant cause of CVA in young adults and are frequently underdiagnosed until severe complications arise.
These conditions highlight the urgent need for advanced diagnostic tools that enable early detection and accurate assessment of these pathologies, contributing to improved clinical management and reduced complications.
What are the current approaches?
Current approaches for the diagnosis and monitoring of vascular diseases integrate advanced imaging techniques, computational methods, and clinical tools to enhance accuracy and efficiency in diagnostic outcomes. Doppler ultrasound is widely used due to its non-invasive nature, speed, and low cost, proving effective in assessing blood flow and detecting arterial obstructions. More sophisticated methods, such as computed tomography angiography and magnetic resonance angiography, provide high-resolution imaging that facilitates the detection of atheroma plaques, arterial dissections, and vascular occlusions (blockages in blood vessels). Additionally, transcranial Doppler ultrasound plays a crucial role in real-time monitoring of cerebral blood flow, proving useful in assessing conditions such as intracranial stenosis (narrowing of arteries within the skull) and other cerebrovascular disorders.
Despite their clinical utility, these techniques present limitations, including operator-dependent settings, image irregularities, and challenges in distinguishing vascular from non-vascular structures in transverse images. These factors can compromise diagnostic accuracy, particularly in cases with image artifacts or noise. Such limitations highlight the urgent need for automated, more robust, and precise solutions that can minimize variability and errors associated with manual analysis, ensuring greater consistency and accuracy in the diagnosis and monitoring of vascular diseases.
What does innovation consist of?
The innovation of this study lies in the development of an advanced methodology for the automatic segmentation of blood vessels in transverse ultrasound images of the carotid artery. The proposed approach structured into three main stages: preprocessing, geometric modelling, and classification based on artificial neural networks.
In the preprocessing stage, the region of interest in the images is binarized, removing noise smaller than 1% of the total pixels. The identification of the vascular lumen (the internal space of blood vessels) is optimized by maximizing three parameters: the circularity index, which evaluates the circular shape of structures; the irregularity index, which penalizes irregular contours; and the centrality index, which prioritizes structures positioned at the centre of the image. This combination enables a robust segmentation, accurately distinguishing the lumen from the vessel wall and assigning greyscale levels to highlight the regions of interest.
In the geometric modelling stage, Bézier curves used to smooth vascular contours. The contour pixels are organized into upper and lower subsets based on their vertical orientation, allowing for the creation of two Bézier curves that then merged to form a continuous and precise outline of the vessel walls.
In the classification stage, AI plays a key role. An advanced neural network is applied to analyse candidate regions for blood vessels, determining with high accuracy the presence or absence of vascular structures. This network was trained and tested using real ultrasound images of varying quality and acquisition settings, ensuring the model’s robustness and generalization capability across different clinical scenarios. During training, dynamic adjustments to the neural network’s internal parameters enhanced precision and minimized classification errors. Additionally, data augmentation techniques, such as horizontal image flipping, were employed to expand the dataset, significantly increasing the volume of information available for model training and validation.
What are the main results? What is the future of this approach?
Although this study does not present detailed quantitative data, the results obtained from training the model with real ultrasound images demonstrate significant advances in the accuracy and efficiency of vascular disease diagnosis. The integration of artificial neural networks in this approach enables the automated classification of candidate regions, ensuring a reliable distinction between blood vessels and adjacent structures, even in suboptimal image quality or challenging clinical conditions. Additionally, the use of Bézier curves in geometric modelling allows for the generation of precise and continuous contours of vascular walls, enhancing the detection and delineation of vascular structures. This balance between image processing techniques and AI stands out as a robust and adaptable approach that overcomes the limitations of traditional methods, such as operator-dependent settings, image noise interference, and the difficulty in distinguishing vascular from non-vascular structures.
The future of this approach is promising for transforming vascular diagnostics. Expanding the training dataset could improve the model’s generalization, making it more robust and effective across different populations and clinical settings. Its implementation in hospital software and portable ultrasound devices could enable faster, more accessible, and real-time diagnostics, particularly in regions with limited medical resources. Furthermore, this methodology can be adapted to analyse other vessels beyond the carotid artery, such as coronary and peripheral arteries, expanding its impact on cardiovascular pathologies. When combined with other imaging modalities, such as computed tomography and magnetic resonance imaging, this approach could provide more detailed and comprehensive diagnoses. Additionally, integrating clinical data, biomarkers, and demographic information allows for personalized diagnostics, aligning with the trend toward precision medicine. This innovation optimizes clinical workflows and reinforces the relevance of AI in digital health.
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