941 research outputs found
Coronary Artery Centerline Extraction in Cardiac CT Angiography Using a CNN-Based Orientation Classifier
Coronary artery centerline extraction in cardiac CT angiography (CCTA) images
is a prerequisite for evaluation of stenoses and atherosclerotic plaque. We
propose an algorithm that extracts coronary artery centerlines in CCTA using a
convolutional neural network (CNN).
A 3D dilated CNN is trained to predict the most likely direction and radius
of an artery at any given point in a CCTA image based on a local image patch.
Starting from a single seed point placed manually or automatically anywhere in
a coronary artery, a tracker follows the vessel centerline in two directions
using the predictions of the CNN. Tracking is terminated when no direction can
be identified with high certainty.
The CNN was trained using 32 manually annotated centerlines in a training set
consisting of 8 CCTA images provided in the MICCAI 2008 Coronary Artery
Tracking Challenge (CAT08). Evaluation using 24 test images of the CAT08
challenge showed that extracted centerlines had an average overlap of 93.7%
with 96 manually annotated reference centerlines. Extracted centerline points
were highly accurate, with an average distance of 0.21 mm to reference
centerline points. In a second test set consisting of 50 CCTA scans, 5,448
markers in the coronary arteries were used as seed points to extract single
centerlines. This showed strong correspondence between extracted centerlines
and manually placed markers. In a third test set containing 36 CCTA scans,
fully automatic seeding and centerline extraction led to extraction of on
average 92% of clinically relevant coronary artery segments.
The proposed method is able to accurately and efficiently determine the
direction and radius of coronary arteries. The method can be trained with
limited training data, and once trained allows fast automatic or interactive
extraction of coronary artery trees from CCTA images.Comment: Accepted in Medical Image Analysi
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Deep learning for cardiac image segmentation: A review
Deep learning has become the most widely used approach for cardiac image segmentation in recent years. In this paper, we provide a review of over 100 cardiac image segmentation papers using deep learning, which covers common imaging modalities including magnetic resonance imaging (MRI), computed tomography (CT), and ultrasound (US) and major anatomical structures of interest (ventricles, atria and vessels). In addition, a summary of publicly available cardiac image datasets and code repositories are included to provide a base for encouraging reproducible research. Finally, we discuss the challenges and limitations with current deep learning-based approaches (scarcity of labels, model generalizability across different domains, interpretability) and suggest potential directions for future research
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Blood Vessel Segmentation and shape analysis for quantification of Coronary Artery Stenosis in CT Angiography
This thesis presents an automated framework for quantitative vascular shape analysis of the coronary arteries, which constitutes an important and fundamental component of an automated image-based diagnostic system. Firstly, an automated vessel segmentation algorithm is developed to extract the coronary arteries based on the framework of active contours. Both global and local intensity statistics are utilised in the energy functional calculation, which allows for dealing with non-uniform brightness conditions, while evolving the contour towards to the desired boundaries without being trapped in local minima. To suppress kissing vessel artifacts, a slice-by-slice correction scheme, based on multiple regions competition, is proposed to identify and track the kissing vessels throughout the transaxial images of the CTA data. Based on the resulting segmentation, we then present a dedicated algorithm to estimate the geometric parameters of the extracted arteries, with focus on vessel bifurcations. In particular, the centreline and associated reference surface of the coronary arteries, in the vicinity of arterial bifurcations, are determined by registering an elliptical cross sectional tube to the desired constituent branch. The registration problem is solved by a hybrid optimisation method, combining local greedy search and dynamic programming, which ensures the global optimality of the solution and permits the incorporation of any hard constraints posed to the tube model within a natural and direct framework. In contrast with conventional volume domain methods, this technique works directly on the mesh domain, thus alleviating the need for image upsampling. The performance of the proposed framework, in terms of efficiency and accuracy, is demonstrated on both synthetic and clinical image data. Experimental results have shown that our techniques are capable of extracting the major branches of the coronary arteries and estimating the related geometric parameters (i.e., the centreline and the reference surface) with a high degree of agreement to those obtained through manual delineation. Particularly, all of the major branches of coronary arteries are successfully detected by the proposed technique, with a voxel-wise error at 0.73 voxels to the manually delineated ground truth data. Through the application of the slice-by-slice correction scheme, the false positive metric, for those coronary segments affected by kissing vessel artifacts, reduces from 294% to 22.5%. In terms of the capability of the presented framework in defining the location of centrelines across vessel bifurcations, the mean square errors (MSE) of the resulting centreline, with respect to the ground truth data, is reduced by an average of 62.3%, when compared with initial estimation obtained using a topological thinning based algorithm
Computerized analysis of coronary artery disease: Performance evaluation of segmentation and tracking of coronary arteries in CT angiograms
Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/134780/1/mp0294.pd
Coronary Artery Segmentation and Motion Modelling
Conventional coronary artery bypass surgery requires invasive sternotomy and the
use of a cardiopulmonary bypass, which leads to long recovery period and has high
infectious potential. Totally endoscopic coronary artery bypass (TECAB) surgery
based on image guided robotic surgical approaches have been developed to allow the
clinicians to conduct the bypass surgery off-pump with only three pin holes incisions
in the chest cavity, through which two robotic arms and one stereo endoscopic camera
are inserted. However, the restricted field of view of the stereo endoscopic images leads
to possible vessel misidentification and coronary artery mis-localization. This results
in 20-30% conversion rates from TECAB surgery to the conventional approach.
We have constructed patient-specific 3D + time coronary artery and left ventricle
motion models from preoperative 4D Computed Tomography Angiography (CTA)
scans. Through temporally and spatially aligning this model with the intraoperative
endoscopic views of the patient's beating heart, this work assists the surgeon to identify
and locate the correct coronaries during the TECAB precedures. Thus this work has
the prospect of reducing the conversion rate from TECAB to conventional coronary
bypass procedures.
This thesis mainly focus on designing segmentation and motion tracking methods
of the coronary arteries in order to build pre-operative patient-specific motion models.
Various vessel centreline extraction and lumen segmentation algorithms are presented,
including intensity based approaches, geometric model matching method and
morphology-based method. A probabilistic atlas of the coronary arteries is formed
from a group of subjects to facilitate the vascular segmentation and registration procedures.
Non-rigid registration framework based on a free-form deformation model
and multi-level multi-channel large deformation diffeomorphic metric mapping are
proposed to track the coronary motion. The methods are applied to 4D CTA images
acquired from various groups of patients and quantitatively evaluated
In Vivo MRI-Based Three-Dimensional Fluid-Structure Interaction Models and Mechanical Image Analysis for Human Carotid Atherosclerotic Plaques
Introduction. Atherosclerotic plaque rupture may occur without warning leading to severe clinical events such as heart attack and stroke. The mechanisms causing plaque rupture are not well understood. It is hypothesized that mechanical forces may play an important role in the plaque rupture process and that image-based computational mechanical analysis may provide useful information for more accurate plaque vulnerability assessment. The objectives of this dissertation are: a) develop in vivo magnetic resonance imaging (MRI)-based 3D computational models with fluid-structure Interactions (FSI) for human atherosclerotic carotid plaques; b) perform mechanical analysis using 3D FSI models to identify critical stress/strain conditions which may be used for possible plaque rupture predictions. Data, Model, and Methods. Histological, ex vivo/ in vivo MRI data of human carotid plaques were provided by the University of Washington Medical School and Washington University Medical School. Blood flow was assumed to be laminar, Newtonian, viscous and incompressible. The Navier-Stokes equations with arbitrary Lagrangian-Eulerian (ALE) formulation were used as the governing equations for the flow model. The vessel and plaque components were assumed to be hyperelastic, isotropic, nearly-incompressible and homogeneous. The nonlinear Mooney-Rivlin model was used to describe the nonlinear properties of the materials with parameter values chosen to match available experimental data. The fully-coupled FSI models were solved by a commercial finite element software ADINA to obtain full 3D flow and stress/strain distributions for analysis. Validation of the computational models and Adina software were provided by comparing computational solutions with analytic solutions and experimental data. Several novel methods were introduced to address some fundamental issues for construction of in vivo MRI-based 3D FSI models: a) an automated MRI segmentation technique using a Bayes theorem with normal probability distribution was implemented to obtain plaque geometry with enclosed components; b) a pre-shrink process was introduced to shrink the in vivo MRI geometry to obtain the no-load shape of the plaque; c) a Volume Component-Fitting Method was introduced to generate a 3D computational mesh for the plaque model with deformable complex geometry, FSI and inclusions; d) a method using MRI data obtained under in vitro pressurized conditions was introduced to determine vessel material properties. Results. The effects of material properties on flow and wall stress/strain behaviors were evaluated. The results indicate that a 100% stiffness increase may decrease maximal values of maximum principal stress (Stress-P1) and maximum principal strain (Strain-P1) by about 20% and 40%, respectively; flow Maximum-Shear-Stress (FMSS) and flow velocity did not show noticeable changes. By comparing ex vivo and in vivo data of 10 plaque samples, the average axial (25%) and inner circumferential (7.9%) shrinkages of the plaques between loaded and unloaded state were obtained. Effects of the shrink-stretch process on plaque stress/strain distributions were demonstrated based on six adjusted 3D FSI models with different shrinkages. Stress-P1 and Strain-P1 increased 349.8% and 249% respectively with 33% axial stretch. The effects of a lipid-rich necrotic core and fibrous cap thickness on structure/flow behaviors were investigated. The mean values of wall Stress-P1 and Strain-P1 from lipid nodes from a ruptured plaque were significantly higher than those from a non-ruptured plaque (112.3 kPa, 0.235 & 80.1 kPa, 0.185), which was 40.2% and 26.8% higher, respectively (p\u3c0.001). High stress/strain concentrations were found at the thin fibrous cap regions. These results indicate that high stress concentrations and thin fibrous cap thickness might be critical indicators for plaque vulnerability. Conclusion. In vivo image-based 3D FSI models and mechanical image analysis may have the potential to provide quantitative risk indicators for plaque vulnerability assessment
Deep Learning in Cardiology
The medical field is creating large amount of data that physicians are unable
to decipher and use efficiently. Moreover, rule-based expert systems are
inefficient in solving complicated medical tasks or for creating insights using
big data. Deep learning has emerged as a more accurate and effective technology
in a wide range of medical problems such as diagnosis, prediction and
intervention. Deep learning is a representation learning method that consists
of layers that transform the data non-linearly, thus, revealing hierarchical
relationships and structures. In this review we survey deep learning
application papers that use structured data, signal and imaging modalities from
cardiology. We discuss the advantages and limitations of applying deep learning
in cardiology that also apply in medicine in general, while proposing certain
directions as the most viable for clinical use.Comment: 27 pages, 2 figures, 10 table
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