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Automatic construction of multipleobject threedimensional statistical shape models: application to cardiac modelling
 IEEE Transactions on Medical Imaging
, 2002
"... Abstract—A novel method is introduced for the generation of landmarks for threedimensional (3D) shapes and the construction of the corresponding 3D statistical shape models. Automatic landmarking of a set of manual segmentations from a class of shapes is achieved by 1) construction of an atlas of ..."
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Cited by 83 (10 self)
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Abstract—A novel method is introduced for the generation of landmarks for threedimensional (3D) shapes and the construction of the corresponding 3D statistical shape models. Automatic landmarking of a set of manual segmentations from a class of shapes is achieved by 1) construction of an atlas of the class, 2) automatic extraction of the landmarks from the atlas, and 3) subsequent propagation of these landmarks to each example shape via a volumetric nonrigid registration technique using multiresolution Bspline deformations. This approach presents some advantages over previously published methods: it can treat multiplepart structures and requires less restrictive assumptions on the structure’s topology. In this paper, we address the problem of building a 3D statistical shape model of the left and right ventricle of the heart from 3D magnetic resonance images. The average accuracy in landmark propagation is shown to be below 2.2 mm. This application demonstrates the robustness and accuracy of the method in the presence of large shape variability and multiple objects. Index Terms—Atlas, cardiac models, modelbased image analysis, nonrigid registration, statistical shape models. I.
4D deformable models with temporal constraints: application to 4D cardiac image segmentation
, 2005
"... ..."
A parametric deformable model to fit unstructured 3D data
, 1995
"... Recovery of unstructured 3D data with deformable models has been the subject of many studies over the last ten years. In particular, in medical image understanding, deformable models are useful to get a precise representation of anatomical structures. However, general deformable models involve large ..."
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Cited by 53 (1 self)
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Recovery of unstructured 3D data with deformable models has been the subject of many studies over the last ten years. In particular, in medical image understanding, deformable models are useful to get a precise representation of anatomical structures. However, general deformable models involve large linear systems to solve when dealing with high resolution 3D images. The advantage of parametric deformable models like superquadrics is their small number of parameters to describe a shape combined with a better robustness in the presence of noise or sparse data. Also, at the expense of a reasonable number of additional parameters, free form deformations provide a much closer fit and a volumetric deformation field. This article introduces such a model to fit unstructured 3D points with a parametric deformable surface based on a superquadric fit followed by a free form deformation to describe the cardiac left ventricle. We present the mathematical and algorithmic details of the method, as wel...
Tracking And Motion Analysis Of The Left Ventricle With Deformable Superquadrics
 Medical Image Analysis
, 1996
"... We present a new approach to analyse the deformation of the left ventricle of the heart based on a parametric model that gives a compact representation of a set of points in a 3D image. We present a strategy for tracking surfaces in a sequence of 3D cardiac images. Following tracking, we then i ..."
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Cited by 52 (8 self)
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We present a new approach to analyse the deformation of the left ventricle of the heart based on a parametric model that gives a compact representation of a set of points in a 3D image. We present a strategy for tracking surfaces in a sequence of 3D cardiac images. Following tracking, we then infer quantitative parameters which characterize: left ventricle motion, volume of left ventricle, ejection fraction, amplitude and twist component of cardiac motion. We explain the computation of these parameters using our model. Experimental results are shown in time sequences of two modalities of medical images, nuclear medicine and Xray computed tomography (CT). Video sequences presenting these results are on the CDROM.
Medical computer vision, virtual reality and robotics
 Image and Vision Computing
, 1995
"... The automated analysis of 3D medical images can improve both diagnosis and therapy significantly. This automation raises a number of new fascinating research problems in the fields of computer vision, graphics and robotics. In this paper, I propose a list of such problems after a review of the curre ..."
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Cited by 49 (9 self)
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The automated analysis of 3D medical images can improve both diagnosis and therapy significantly. This automation raises a number of new fascinating research problems in the fields of computer vision, graphics and robotics. In this paper, I propose a list of such problems after a review of the current major 3D imaging modalities, and a description of the related medical needs. I then present some of the past and current work done in our research group EPIDAURE * at INRIA, on the following topics: segmentation of 3D images; 3D shape modelling; 3D rigid and nonrigid registration; 3D motion analysis; and 3D simulation of therapy. Most topics are discussed in a synthetic manner, and illustrated by results. Rigid matching is treated more thoroughly as an illustration of a transfer from computer vision towards 3D image processing. The later topics are illustrated by preliminary results, and a number of promising research tracks are suggested.
Definition of a 4D continuous planispheric transformation for the tracking and the analysis of LV motion
, 1998
"... Cardiologists assume that analysis of the motion of the heart (especially the left ventricle) can give some information about the health of the myocardium. A 4D polar transformation is defined to describe the left ventricle (LV) motion and a method is presented to estimate it from sequences of 3D im ..."
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Cited by 20 (1 self)
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Cardiologists assume that analysis of the motion of the heart (especially the left ventricle) can give some information about the health of the myocardium. A 4D polar transformation is defined to describe the left ventricle (LV) motion and a method is presented to estimate it from sequences of 3D images. The transformation is defined in 3Dplanispheric coordinates (3PC) by a small number of parameters involved in a set of simple linear equations. It is continuous and regular in time and space, periodicity in time can be imposed. The local motion can be easily decomposed into a few canonical motions (radial motion, rotation around the longaxis, elevation). To recover the motion from original data, the 4D polar transformation is calculated using an adaptation of the Iterative Closest Point algorithm. We present the mathematical framework and a demonstration of its feasability on a series of gated SPECT sequences.
Multiframe Temporal Estimation of Cardiac Nonrigid Motion
 IEEE Trans. Med. Imag
, 2000
"... A robust, flexible system for tracking the point to point nonrigid motion of the left ventricular (LV) endocardial wall in image sequences has been developed. This system is unique in its ability to model motion trajectories across multiple frames. The foundation of this system is an adaptive transv ..."
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Cited by 16 (0 self)
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A robust, flexible system for tracking the point to point nonrigid motion of the left ventricular (LV) endocardial wall in image sequences has been developed. This system is unique in its ability to model motion trajectories across multiple frames. The foundation of this system is an adaptive transversal filter based on the recursive leastsquares algorithm. This filter facilitates the integration of models for periodicity and proximal smoothness as appropriate using a contourbased description of the object's boundaries. A set of correspondences between contours and an associated set of correspondence quality measures comprise the input to the system. Frametoframe relationships from two different frames of reference are derived and analyzed using synthetic and actual images. Two multiframe temporal models, both based on a sum of sinusoids, are derived. Illustrative examples of the system's output are presented for quantitative analysis. Validation of the system is performed by compa...
Epidaure: a Research Project in Medical Image Analysis, Simulation and Robotics at INRIA
, 2003
"... INTRODUCTION E PIDAURE is the name of a research project launched in 1989 at INRIA Rocquencourt, close to Paris, France. At that time, after a first experience of research in Computer Vision [1] in the group of O. Faugeras, I was very enthusiastic about the idea of transposing research resul ..."
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Cited by 16 (4 self)
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INTRODUCTION E PIDAURE is the name of a research project launched in 1989 at INRIA Rocquencourt, close to Paris, France. At that time, after a first experience of research in Computer Vision [1] in the group of O. Faugeras, I was very enthusiastic about the idea of transposing research results of digital image analysis into the medical domain. Visiting hospitals and medical research centers, I was progressively convinced that Medical Image Analysis was an important research domain by itself. In fact I had the impression that a better exploitation of the available medical imaging modalities would require more and more advanced image processing tools in the short and longterm future, not only to assess the diagnosis on more objective and quantitative measurements, but also to better prepare, control and evaluate the therapy. Fig. 1. This image has been the "Logo" of the Epidaure project for a long time. It was also used as a logo of the first CVRMed Conference held in Nice in 1
Tracking medical 3D data with a deformable parametric model
 In European Conference on Computer Vision
, 1996
"... We present a new approach to surface tracking applied to 3D medical data with a deformable model. It is based on a parametric model composed of a superquadric fit followed by a FreeForm Deformation (FFD), that gives a compact representation of a set of points in a 3D image. We present three dioeere ..."
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Cited by 11 (1 self)
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We present a new approach to surface tracking applied to 3D medical data with a deformable model. It is based on a parametric model composed of a superquadric fit followed by a FreeForm Deformation (FFD), that gives a compact representation of a set of points in a 3D image. We present three dioeerent approaches to track surfaces in a sequence of 3D cardiac images. From the tracking, we infer quantitative parameters which are useful for the physician, like the ejection fraction, the variation of the heart wall thickness and of the volume during a cardiac cycle or the torsion component in the deformation of the ventricle. Experimental results are shown for automatic shape tracking and motion analysis of a time sequence of Nuclear Medicine images.
Definition of a 4D continuous polar transformation for the tracking and the analysis of LV motion
"... A 4D polar transformation is defined to describe the left ventricle (LV) motion and a method is presented to estimate it from sequences of 3D images. The transformation is defined in 3Dplanispheric coordinates by a small number of parameters involved in a set of simple linear equations. It is conti ..."
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Cited by 10 (3 self)
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A 4D polar transformation is defined to describe the left ventricle (LV) motion and a method is presented to estimate it from sequences of 3D images. The transformation is defined in 3Dplanispheric coordinates by a small number of parameters involved in a set of simple linear equations. It is continuous and regular in time and space, periodicity in time can be imposed. The local motion can be easily decomposed into a few canonical motions (centripetal contraction, rotation around the longaxis, elevation). To recover the motion from original data, the 4D polar transformation is calculated using an adaptation of the Iterative Closest Point algorithm. We present the mathematical framework and a demonstration of its feasability on a gated SPECT sequence.