Segmentation of the left and right cardiac ventricle using a combined bi-temporal statistical model

@inproceedings{Fritz2006SegmentationOT,
  title={Segmentation of the left and right cardiac ventricle using a combined bi-temporal statistical model},
  author={Dominik Fritz and Daniel Rinck and R{\"u}diger Dillmann and Michael Scheuering},
  booktitle={SPIE Medical Imaging},
  year={2006}
}
The manual segmentation and analysis of high-resolution multislice cardiac CT datasets is both labor intensive and time consuming. Therefore it is necessary to supply the cardiologist with powerful software tools to segment the myocardium as well as the cardiac cavities and to compute the relevant diagnostic parameters. In this paper we present an automatic cardiac segmentation procedure with minimal user interaction. It is based on a combined bi-temporal statistical model of the left and right… 
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37 Citations

Automatic 4D segmentation of the left ventricle in cardiac-CT-data
TLDR
A novel approach for the 4D segmentation of the left ventricle in cardiac-CT-data uses a novel approach based on the individual volume curve for the interpolation between ED and ES and afterwards an active contour algorithm for the final segmentation.
Right Ventricle Segmentation by Temporal Information Constrained Gradient Vector Flow
  • XuLei Yang, S. Yeo, R. Tan
  • Computer Science
    2013 IEEE International Conference on Systems, Man, and Cybernetics
  • 2013
TLDR
This paper presents a 2D + T active contour model for segmentation and tracking of RV endocardium on cardiac magnetic resonance (MR) images and proposes to integrate the time-dependent constraints into the energy functional of the classical gradient vector flow (GVF).
Four-Chamber Heart Modeling and Automatic Segmentation for 3-D Cardiac CT Volumes Using Marginal Space Learning and Steerable Features
TLDR
An automatic four-chamber heart segmentation system for the quantitative functional analysis of the heart from cardiac computed tomography (CT) volumes is proposed and an efficient and robust approach for automatic heart chamber segmentation in 3D CT volumes is developed.
User intervention based segmentation of Myocardium In cardiac cine MRI images
TLDR
This work presents user intervention based semi-automatic method for segmentation in short axis images, which includes various segmentation methods like graph-cut, watershed and the threshold based segmentation to calculate wall thickness and ejection factor which are of clinical importance.
A fully automatic cardiac segmentation method using region growing technique
TLDR
A fully automatic cardiac segmentation of the right ventricle (RV) based on region growing (RG) technique, which can segment the area of the RV from the basal to the apical slices at End-systolic (ES) and Enddiastolic (ED) phases is presented.
A Novel Model-Based 3D ${+}$Time Left Ventricular Segmentation Technique
A common approach to model-based segmentation is to assume a top-down modelling strategy. However, this is not feasible for complex 3D +time structures, such as the cardiac left ventricle, due to…
Overview of the Whole Heart and Heart Chamber Segmentation Methods.
TLDR
A comprehensive and critical overview of research on the whole heart, bi-ventricles and left atrium segmentation methods from computed tomography, magnetic resonance imaging and echocardiography imaging is presented.
Left ventricle myocardium segmentation in multi-slice computerized tomography
TLDR
A left ventricle myocardium segmentation method in multi-slice computerized tomography images based on level-sets deformable contours is described, intended for incorporation within a software platform for visualization and processing of cardiac images.
Left ventricle segmentation from MSCT data based on random walks approach
TLDR
An improved robust semi-automated approach is presented for segmentation of cavity and myocardium from 3D cardiac multi-slice CT (MSCT) dataset based on random walks and 6-connected lattice topology and Conjugate Gradient method have been applied in the random walker algorithm to promote the segmentation performance of 3D dataset.
Aortic and Mitral Valve Modeling From Multi-Modal Image Data
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