Graph Convolutional Networks for Coronary Artery Segmentation in Cardiac CT Angiography

Wolterink, Jelmer M.; Leiner, Tim; Išgum, Ivana

doi:10.1007/978-3-030-35817-4_8

Cited by 55 publications

(21 citation statements)

References 9 publications

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“…Graph convolutional networks have also been investigated by Wolterink et al [ 137 ] for coronary artery segmentation in CTA. The authors proposed to use GCNs to directly optimize the position of the tubular surface mesh vertices.…”

Section: Case Studies Of Gnn For Medical Diagnosis and Analysismentioning

confidence: 99%

Graph-Based Deep Learning for Medical Diagnosis and Analysis: Past, Present and Future

Ahmedt-Aristizabal

Armin

Denman

et al. 2021

Sensors

148

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With the advances of data-driven machine learning research, a wide variety of prediction problems have been tackled. It has become critical to explore how machine learning and specifically deep learning methods can be exploited to analyse healthcare data. A major limitation of existing methods has been the focus on grid-like data; however, the structure of physiological recordings are often irregular and unordered, which makes it difficult to conceptualise them as a matrix. As such, graph neural networks have attracted significant attention by exploiting implicit information that resides in a biological system, with interacting nodes connected by edges whose weights can be determined by either temporal associations or anatomical junctions. In this survey, we thoroughly review the different types of graph architectures and their applications in healthcare. We provide an overview of these methods in a systematic manner, organized by their domain of application including functional connectivity, anatomical structure, and electrical-based analysis. We also outline the limitations of existing techniques and discuss potential directions for future research.

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Section: Case Studies Of Gnn For Medical Diagnosis and Analysismentioning

confidence: 99%

Graph-Based Deep Learning for Medical Diagnosis and Analysis: Past, Present and Future

Ahmedt-Aristizabal

Armin

Denman

et al. 2021

Sensors

148

View full text Add to dashboard Cite

show abstract

“…Recent studies have demonstrated that neural network segmentation accuracy can be improved by directly predicting the boundary points of the structure of interest using a polar coordinates formulation, as opposed to binary labeling of pixels in the input image .32,48,49 However, these studies were limited to natural images 49 and the coronary arteries. 32,48 In particular 48 used a ray-casting formulation which requires a-priori specification of a region of interest which is nontrivial to apply across different anatomical regions containing vessels with substantial radius variation.…”

Section: Introductionmentioning

confidence: 99%

Neural Network Vessel Lumen Regression for Automated Lumen Cross-Section Segmentation in Cardiovascular Image-Based Modeling

Maher

Parker

Wilson

et al. 2020

Cardiovasc Eng Tech

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Purpose-We accelerate a pathline-based cardiovascular model building method by training machine learning models to directly predict vessel lumen surface points from computed tomography (CT) and magnetic resonance (MR) medical image data. Methods-We formulate vessel lumen detection as a regression task using a polar coordiantes representation. Results-Neural networks trained with our regression formulation allow predictions to be made with significantly higher accuracy than existing methods that identify the vessel lumen through binary pixel classification. The regression formulation enables machine learning models to be trained end-to-end for vessel lumen detection without post-processing steps that reduce accuracy. Conclusion-By employing our models in a pathline-based cardiovascular model building pipeline we substantially reduce the manual segmentation effort required to build accurate cardiovascular models, and reduce the overall time required to perform patient-specific cardiovascular simulations. While our method is applied here for cardiovascular model building it is generally applicable to segmentation of tree-like and tubular structures from image data.

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“…The DSC accuracy is between 76%–78%. Wolterink et al [35] proposed using graph convolutional networks to predict the spatial location of vertices in a tubular surface mesh that segments the coronary artery lumen. The average DSC is 74%.…”

Section: Introductionmentioning

confidence: 99%

A Computationally Efficient Approach to Segmentation of the Aorta and Coronary Arteries Using Deep Learning

et al. 2021

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Early detection and diagnosis of coronary artery disease could reduce the risk of developing a heart attack. The coronary arteries are optimally visualised using computed tomography coronary angiography (CTCA) imaging. These images are reviewed by specialist radiologists who evaluate the coronary arteries for potential narrowing. A lack of radiologists in the UK is a constraint to timely diagnosis of coronary artery disease, particularly in the acute accident and emergency department setting. The development of automated methods by which coronary artery narrowing can be identified rapidly and accurately are therefore timely. Such complex computer-based tools also need to be sufficiently computationally efficient that they can run on servers typically found in hospital settings, where graphical processing units for example are unavailable. We propose a fully automatic two-dimensional Unet model to segment the aorta and coronary arteries on CTCA images. Two models are trained to segment two regions of interest, (1) the aorta and the coronary arteries or (2) the coronary arteries alone. Our method achieves 91.20% and 88.80% dice similarity coefficient accuracy on regions of interest 1 and 2 respectively.Compared with a semi-automatic segmentation method, our model performs better when segmenting the coronary arteries alone. The performance of the proposed method is comparable to existing published twodimensional or three-dimensional deep learning models. Importantly, the algorithmic and graphical processing unit memory efficiencies are maintained such that the model can be deployed without requiring graphical processing units, and therefore can be used in a hospital setting.

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Graph Convolutional Networks for Coronary Artery Segmentation in Cardiac CT Angiography

Cited by 55 publications

References 9 publications

Graph-Based Deep Learning for Medical Diagnosis and Analysis: Past, Present and Future

Graph-Based Deep Learning for Medical Diagnosis and Analysis: Past, Present and Future

Neural Network Vessel Lumen Regression for Automated Lumen Cross-Section Segmentation in Cardiovascular Image-Based Modeling

A Computationally Efficient Approach to Segmentation of the Aorta and Coronary Arteries Using Deep Learning

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