A Unified Statistical/Deterministic Deformable Model for LV Segmentation in Cardiac MRI
نویسندگان
چکیده
We propose a novel deformable model with statistical and deterministic components for LV segmentation in cardiac magnetic resonance (MR) cine images. The statistical deformable component learns a global reference model of the LV using Principal Component Analysis (PCA) while the deterministic deformable component consists of a finite-element deformable surface superimposed on the reference model. The statistical model accounts for most of the global variations in shape found in the training set while the deterministic skin accounts for the local deformations consistent with the detailed image features. Intensity gradient-based image forces are applied to the model to segment and reconstruct LV shape. We validate our model on the MICCAI Grand Challenge dataset using leave-one-out training. Comparing the automated segmentation to the manual segmentation yields a Mean Perpendicular Distance (MPD) of 3.65 mm and a Dice coefficient of 0.86.
منابع مشابه
Automated Model-Based Left Ventricle Segmentation in Cardiac MR Images
We present a fully automated system for segmenting the Left Ventricle (LV) in cardiac MR images based on statistical and deformable models. A Project-Out Inverse Compositional Active Appearance Model of 3D LV shape produces segmentations that are refined using a unified statistical/deterministic deformable model. A new multi-scale detector, based on the Histogram of Oriented Gradients (HoG), pr...
متن کاملA combined deep-learning and deformable-model approach to fully automatic segmentation of the left ventricle in cardiac MRI
Segmentation of the left ventricle (LV) from cardiac magnetic resonance imaging (MRI) datasets is an essential step for calculation of clinical indices such as ventricular volume and ejection fraction. In this work, we employ deep learning algorithms combined with deformable models to develop and evaluate a fully automatic LV segmentation tool from short-axis cardiac MRI datasets. The method em...
متن کاملCombining active appearance and deformable superquadric models for LV segmentation in cardiac MRI
In this work we automatically segment the left ventricle (LV) in cardiac MR images in the end-diastole (ED) and end-systole (ES) phases using a novel approach that combines statistical and deterministic deformable models. A 3D Active Appearance Model (AAM) is used to segment the ED phase. The AAM texture model is trained on radial samples from gradient magnitude images to make the fitting proce...
متن کاملUnified Deterministic/Statistical Deformable Models for Cardiac Image Analysis
OF THE DISSERTATION Unified Deterministic/Statistical Deformable Models for Cardiac Image Analysis by Sharath Kumar Gopal Doctor of Philosophy in Computer Science University of California, Los Angeles, 2016 Professor Demetri Terzopoulos, Chair This thesis proposes to fully automate the shape and motion reconstruction of non-rigid objects from visual information using a unified deterministic/sta...
متن کاملCardiac Segmentation from MRI-Tagged and CT Images
We present new developments in the formulation of a new class of deformable models, which we term MetaMorphs, and demonstrate the effectiveness of the method in cardiac image segmentation and motion analysis. The formulation of the MetaMorphs naturally integrates both shape and interior texture, and the model deformations are derived from both boundary and region information based on a variatio...
متن کامل