Three-dimensional modeling for functional analysis of cardiac images, a review

By Alejandro F Frangi, Wiro J. Niessen, Max A. Viergever. IEEE Transactions on Medical Imaging, 2001

632 citations recorded by the source corpus.

Subfields: Computational Mechanics, Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/0e97cfdd5e586878a3c589e63f2758975dfc4bf4

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In Computational Mechanics

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In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

Noninvasive Transmural Electrophysiological Imaging Based on Minimization of Total-Variation Functional (2014, methodology): “However, while methods for noninvasive imaging and analysis of 3-D structure [2], kinetics [3], and mechanics [4] of an individual’s heart have substantially improved in recent years, methods for assessing the electrophysiological (EP) aspects have not.”
Ultrasound image segmentation: a survey (2006, background): “See the review by Frangi et al. [7] for a discussion of this.”
Automated Segmentation of the Left Ventricle in Cardiac MRI (2003, background): “Cardiac MRI provides important information for diagnosis and treatment of cardiovascular diseases by enabling quantitative assessment of functional parameters [4].”
Atlas-Based Quantification of Cardiac Remodeling Due to Myocardial Infarction (2014, background): “One particular shape representation is a finite element model, which provides an efficient and accurate representation of complex geometries [11].”
Four-Chamber Heart Modeling and Automatic Segmentation for 3-D Cardiac CT Volumes Using Marginal Space Learning and Steerable Features (2008, neutral): “, the ventricular ejection fraction, myocardium wall thickness, and dissynchrony within a chamber or between different chambers [2].”
A review of cardiac image registration methods (2002, result): “Instead, we refer to some recent papers in this field [14], [20]–[23].”
3-D active appearance models: segmentation of cardiac MR and ultrasound images (2002, neutral): “proaches is provided in [1].”
Fully convolutional multi‐scale residual DenseNets for cardiac segmentation and automated cardiac diagnosis using ensemble of classifiers (2018, background): “Literature survey on short-axis cardiac cine MR segmentation Petitjean and Dacher (2011); Frangi et al. (2001); Tavakoli and Amini (2013); Peng et al.”
Automatic construction of multiple-object three-dimensional statistical shape models: application to cardiac modeling (2002, background): “, a recent literature survey on 3-D modelbased techniques for functional analysis of cardiac images [14].”
FAME-a flexible appearance modeling environment (2003, background): “The second case study addresses an invariable prerequisite for quantitative functional analysis of the human heart [44], namely”
Efficient and generalizable statistical models of shape and appearance for analysis of cardiac MRI (2008, methodology): “We incorporate knowledge about typical cardiac structure by using a 3D active appearance model (AAM) and a 2D + time active shape model (ASM) of the heart to segment the left ventricle (Frangi et al., 2001).”
Statistical shape modeling of the left ventricle: myocardial infarct classification challenge (2017, background): “A review of shape models can be found in [32].”
Segmentation of 4D Cardiac MR Images Using a Probabilistic Atlas and the EM Algorithm (2003, neutral): “005 diac anatomy (Frangi et al., 2001).”
Deformable Medical Image Registration: A Survey (2013, background): “cardiac and respiratory motion) the key element in diagnosis becomes their spatio-temporal behavior [1, 2].”
A combined deep-learning and deformable-model approach to fully automatic segmentation of the left ventricle in cardiac MRI (2015, methodology): “…imaging (MRI) is now routinely being used for the evaluation of the function and structure of the cardiovascular system (Yuan et al., 2002; Lima and Desai, 2004; Frangi et al., 2001; Petitjean and Dacher, 2011; Tavakoli and Amini, 2013; Heimann and Meinzer, 2009; Suinesiaputra et al., 2014).”