Cardiac MR Image Segmentation Techniques: an overview
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paper_title: Fast, accurate, and fully automatic segmentation of the right ventricle in short-axis cardiac MRI

paper_content:

Abstract This paper presents a fully automatic method to segment the right ventricle (RV) from short-axis cardiac MRI. A combination of a novel window-constrained accumulator thresholding technique, binary difference of Gaussian (DoG) filters, optimal thresholding, and morphology are utilized to drive the segmentation. A priori segmentation window constraints are incorporated to guide and refine the process, as well as to ensure appropriate area confinement of the segmentation. Training and testing were performed using a combined 48 patient datasets supplied by the organizers of the MICCAI 2012 right ventricle segmentation challenge, allowing for unbiased evaluations and benchmark comparisons. Marked improvements in speed and accuracy over the top existing methods are demonstrated.
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paper_title: A review of segmentation methods in short axis cardiac MR images

paper_content:

For the last 15 years, Magnetic Resonance Imaging (MRI) has become a reference examination for cardiac morphology, function and perfusion in humans. Yet, due to the characteristics of cardiac MR images and to the great variability of the images among patients, the problem of heart cavities segmentation in MRI is still open. This paper is a review of fully and semi-automated methods performing segmentation in short axis images using a cardiac cine MRI sequence. Medical background and specific segmentation difficulties associated to these images are presented. For this particularly complex segmentation task, prior knowledge is required. We thus propose an original categorization for cardiac segmentation methods, with a special emphasis on what level of external information is required (weak or strong) and how it is used to constrain segmentation. After reviewing method principles and analyzing segmentation results, we conclude with a discussion and future trends in this field regarding methodological and medical issues.
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paper_title: Gaussian Mixture Models

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Definition A Gaussian Mixture Model (GMM) is a parametric probability density function represented as a weighted sum of Gaussian component densities. GMMs are commonly used as a parametric model of the probability distribution of continuous measurements or features in a biometric system, such as vocal-tract related spectral features in a speaker recognition system. GMM parameters are estimated from training data using the iterative Expectation-Maximization (EM) algorithm or Maximum A Posteriori (MAP) estimation from a well-trained prior model.
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paper_title: Some methods for classification and analysis of multivariate observations

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The main purpose of this paper is to describe a process for partitioning an N-dimensional population into k sets on the basis of a sample. The process, which is called 'k-means,' appears to give partitions which are reasonably efficient in the sense of within-class variance. That is, if p is the probability mass function for the population, S = {S1, S2, * *, Sk} is a partition of EN, and ui, i = 1, 2, * , k, is the conditional mean of p over the set Si, then W2(S) = ff=ISi f z u42 dp(z) tends to be low for the partitions S generated by the method. We say 'tends to be low,' primarily because of intuitive considerations, corroborated to some extent by mathematical analysis and practical computational experience. Also, the k-means procedure is easily programmed and is computationally economical, so that it is feasible to process very large samples on a digital computer. Possible applications include methods for similarity grouping, nonlinear prediction, approximating multivariate distributions, and nonparametric tests for independence among several variables. In addition to suggesting practical classification methods, the study of k-means has proved to be theoretically interesting. The k-means concept represents a generalization of the ordinary sample mean, and one is naturally led to study the pertinent asymptotic behavior, the object being to establish some sort of law of large numbers for the k-means. This problem is sufficiently interesting, in fact, for us to devote a good portion of this paper to it. The k-means are defined in section 2.1, and the main results which have been obtained on the asymptotic behavior are given there. The rest of section 2 is devoted to the proofs of these results. Section 3 describes several specific possible applications, and reports some preliminary results from computer experiments conducted to explore the possibilities inherent in the k-means idea. The extension to general metric spaces is indicated briefly in section 4. The original point of departure for the work described here was a series of problems in optimal classification (MacQueen [9]) which represented special
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paper_title: A connectionist classifier architecture applied to image segmentation

paper_content:

A connectionist classifier architecture that is used in an image segmentation scheme based upon a decision analysis paradigm is described. An appropriate architecture for a connectionist classifier is discussed, and its relationship to other image segmentation architectures following the same paradigm is shown. The performance of a complete system, including pixel descriptor extraction and connectionist classifier, is demonstrated using scenes from industrial inspection and combustion chamber research tasks. >
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paper_title: CONNECTIONIST MODEL BINARIZATION

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Image binarization is a task to convert gray-level images into bi-level ones. Its underlying notion can be simply thought of as threshold selection. However, the result of binarization will cause significant influence on the process of image recognition or understanding. In this paper we discuss a new binarization method, named CMB (connectionist model binarization), which uses the connectionist model. In the method a gray-level histogram is input to a multilayer network trained with the back-propagation algorithm to obtain a threshold which gives a visually suitable binarized image. From the experimental results, it was verified that CMB is an effective binarization method in comparison with other methods.
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paper_title: Fronts propagating with curvature dependent speed: algorithms based on Hamilton–Jacobi formulations

paper_content:

We devise new numerical algorithms, called PSC algorithms, for following fronts propagating with curvature-dependent speed. The speed may be an arbitrary function of curvature, and the front also can be passively advected by an underlying flow. These algorithms approximate the equations of motion, which resemble Hamilton-Jacobi equations with parabolic right-hand sides, by using techniques from hyperbolic conservation laws. Non-oscillatory schemes of various orders of accuracy are used to solve the equations, providing methods that accurately capture the formation of sharp gradients and cusps in the moving fronts. The algorithms handle topological merging and breaking naturally, work in any number of space dimensions, and do not require that the moving surface be written as a function. The methods can be also used for more general Hamilton-Jacobi-type problems. We demonstrate our algorithms by computing the solution to a variety of surface motion problems.
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paper_title: Robust Atlas-Based Segmentation of Highly Variable Anatomy: Left Atrium Segmentation

paper_content:

Automatic segmentation of the heart's left atrium offers great benefits for planning and outcome evaluation of atrial ablation procedures. However, the high anatomical variability of the left atrium presents significant challenges for atlas-guided segmentation. In this paper, we demonstrate an automatic method for left atrium segmentation using weighted voting label fusion and a variant of the demons registration algorithm adapted to handle images with different intensity distributions. We achieve accurate automatic segmentation that is robust to the high anatomical variations in the shape of the left atrium in a clinical dataset of MRA images.
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paper_title: Multi-Atlas-Based Segmentation With Local Decision Fusion—Application to Cardiac and Aortic Segmentation in CT Scans

paper_content:

A novel atlas-based segmentation approach based on the combination of multiple registrations is presented. Multiple atlases are registered to a target image. To obtain a segmentation of the target, labels of the atlas images are propagated to it. The propagated labels are combined by spatially varying decision fusion weights. These weights are derived from local assessment of the registration success. Furthermore, an atlas selection procedure is proposed that is equivalent to sequential forward selection from statistical pattern recognition theory. The proposed method is compared to three existing atlas-based segmentation approaches, namely 1) single atlas-based segmentation, 2) average-shape atlas-based segmentation, and 3) multi-atlas-based segmentation with averaging as decision fusion. These methods were tested on the segmentation of the heart and the aorta in computed tomography scans of the thorax. The results show that the proposed method outperforms other methods and yields results very close to those of an independent human observer. Moreover, the additional atlas selection step led to a faster segmentation at a comparable performance.
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paper_title: An Atlas-Based Segmentation Propagation Framework Using Locally Affine Registration --- Application to Automatic Whole Heart Segmentation

paper_content:

In this paper, we present a novel registration algorithm for locally affine registrations. This method preserves the anatomical and intensity class relationships between the local regions. A regularisation procedure is used to maintain a global diffeomorphic transformation. Combined with a novel generic method for accurately inverting the final deformation field, we include our techniques within an atlas-based segmentation propagation framework. We applied our method to automatically segment the whole heart from cardiac magnetic resonance images from a cohort of 18 volunteers (acquisition resolution 2 × 2 × 2 mm). The results show that the proposed method provides a robust initialisation for the atlas-based segmentation propagation framework refined with a fluid registration. We validated our approach against other registration strategies, and demonstrated that we improved the accuracy of the whole heart segmentations (1.8 ± 0.42 mm).
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paper_title: Multistage hybrid active appearance model matching: segmentation of left and right ventricles in cardiac MR images

paper_content:

A fully automated approach to segmentation of the left and right cardiac ventricles from magnetic resonance (MR) images is reported. A novel multistage hybrid appearance model methodology is presented in which a hybrid active shape model/active appearance model (AAM) stage helps avoid local minima of the matching function. This yields an overall more favorable matching result. An automated initialization method is introduced making the approach fully automated. Our method was trained in a set of 102 MR images and tested in a separate set of 60 images. In all testing cases, the matching resulted in a visually plausible and accurate mapping of the model to the image data. Average signed border positioning errors did not exceed 0.3 mm in any of the three determined contours-left-ventricular (LV) epicardium, LV and right-ventricular (RV) endocardium. The area measurements derived from the three contours correlated well with the independent standard (r = 0.96, 0.96, 0.90), with slopes and intercepts of the regression lines close to one and zero, respectively. Testing the reproducibility of the method demonstrated an unbiased performance with small range of error as assessed via Bland-Altman statistic. In direct border positioning error comparison, the multistage method significantly outperformed the conventional AAM (p < 0.001). The developed method promises to facilitate fully automated quantitative analysis of LV and RV morphology and function in clinical setting.
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paper_title: A review of segmentation methods in short axis cardiac MR images

paper_content:

For the last 15 years, Magnetic Resonance Imaging (MRI) has become a reference examination for cardiac morphology, function and perfusion in humans. Yet, due to the characteristics of cardiac MR images and to the great variability of the images among patients, the problem of heart cavities segmentation in MRI is still open. This paper is a review of fully and semi-automated methods performing segmentation in short axis images using a cardiac cine MRI sequence. Medical background and specific segmentation difficulties associated to these images are presented. For this particularly complex segmentation task, prior knowledge is required. We thus propose an original categorization for cardiac segmentation methods, with a special emphasis on what level of external information is required (weak or strong) and how it is used to constrain segmentation. After reviewing method principles and analyzing segmentation results, we conclude with a discussion and future trends in this field regarding methodological and medical issues.
---
paper_title: Segmentation of Left Ventricle in Cardiac Cine MRI: An Automatic Image-Driven Method

paper_content:

This study investigates a fully automatic left ventricle segmentation method from cine short axis MR images. Advantages of this method include that it: 1) does not require manually drawn initial contours, trained statistical shape or gray-level appearance model; 2) provides not only endocardial and epicardial contours, but also papillary muscles and trabeculations' contours; 3) introduces a roundness measure that is fast and automatically locates the left ventricle; 4) simplifies the epicardial contour segmentation by mapping the pixels from Cartesian to approximately polar coordinates; and 5) applies a fast Fourier transform to smooth the endocardial and epicardial contours. Quantitative evaluation was performed on 41 subjects. The average perpendicular distance between manually drawn and automatically selected contours over all slices, all studies, and two phases (end-diastole and end-systole) was 1.40±1.18 mm for endocardial and 1.75±1.15 mm for epicardial contours. These results indicate a promising method for automatic segmentation of left ventricle for clinical use.
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paper_title: Segmentation of 4D cardiac MR images using a probabilistic atlas and the EM algorithm

paper_content:

In this paper an automatic atlas-based segmentation algorithm for 4D cardiac MR images is proposed. The algorithm is based on the 4D extension of the expectation maximisation (EM) algorithm. The EM algorithm uses a 4D probabilistic cardiac atlas to estimate the initial model parameters and to integrate a priori information into the classification process. The probabilistic cardiac atlas has been constructed from the manual segmentations of 3D cardiac image sequences of 14 healthy volunteers. It provides space and time-varying probability maps for the left and right ventricles, the myocardium, and background structures such as the liver, stomach, lungs and skin. In addition to using the probabilistic cardiac atlas as a priori information, the segmentation algorithm incorporates spatial and temporal contextual information by using 4D Markov Random Fields. After the classification, the largest connected component of each structure is extracted using a global connectivity filter which improves the results significantly, especially for the myocardium. Validation against manual segmentations and computation of the correlation between manual and automatic segmentation on 249 3D volumes were calculated. We used the 'leave one out' test where the image set to be segmented was not used in the construction of its corresponding atlas. Results show that the procedure can successfully segment the left ventricle (LV) (r = 0.96), myocardium (r = 0.92) and right ventricle (r = 0.92). In addition, 4D images from 10 patients with hypertrophic cardiomyopathy were also manually and automatically segmented yielding a good correlation in the volumes of the LV (r = 0.93) and myocardium (0.94) when the atlas constructed with volunteers is blurred.
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paper_title: 4-D Cardiac MR Image Analysis: Left and Right Ventricular Morphology and Function

paper_content:

In this study, a combination of active shape model (ASM) and active appearance model (AAM) was used to segment the left and right ventricles of normal and Tetralogy of Fallot (TOF) hearts on 4-D (3-D+time) MR images. For each ventricle, a 4-D model was first used to achieve robust preliminary segmentation on all cardiac phases simultaneously and a 3-D model was then applied to each phase to improve local accuracy while maintaining the overall robustness of the 4-D segmentation. On 25 normal and 25 TOF hearts, in comparison to the expert traced independent standard, our comprehensive performance assessment showed subvoxel segmentation accuracy, high overlap ratios, good ventricular volume correlations, and small percent volume differences. Following 4-D segmentation, novel quantitative shape and motion features were extracted using shape information, volume-time and dV/dt curves, analyzed and used for disease status classification. Automated discrimination between normal/TOF subjects achieved 90%-100% sensitivity and specificity. The features obtained from TOF hearts show higher variability compared to normal subjects, suggesting their potential use as disease progression indicators. The abnormal shape and motion variations of the TOF hearts were accurately captured by both the segmentation and feature characterization.
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paper_title: Automatic Right Ventricle Segmentation using Multi-Label Fusion in Cardiac MRI

paper_content:

Accurate segmentation of the right ventricle (RV) is a crucial step in the assessment of the ventricular structure and function. Yet, due to its complex anatomy and motion segmentation of the RV has not been as largely studied as the left ventricle. This paper presents a fully automatic method for the segmentation of the RV in cardiac magnetic resonance images (MRI). The method uses a coarse-to-fine segmentation strategy in combination with a multi-atlas propagation segmentation framework. Based on a cross correlation metric, our method selects the best atlases for propagation allowing the refinement of the segmentation at each iteration of the propagation. The proposed method was evaluated on 32 cardiac MRI datasets provided by the RV Segmentation Challenge in Cardiac MRI.
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paper_title: Fast, accurate, and fully automatic segmentation of the right ventricle in short-axis cardiac MRI

paper_content:

Abstract This paper presents a fully automatic method to segment the right ventricle (RV) from short-axis cardiac MRI. A combination of a novel window-constrained accumulator thresholding technique, binary difference of Gaussian (DoG) filters, optimal thresholding, and morphology are utilized to drive the segmentation. A priori segmentation window constraints are incorporated to guide and refine the process, as well as to ensure appropriate area confinement of the segmentation. Training and testing were performed using a combined 48 patient datasets supplied by the organizers of the MICCAI 2012 right ventricle segmentation challenge, allowing for unbiased evaluations and benchmark comparisons. Marked improvements in speed and accuracy over the top existing methods are demonstrated.
---
paper_title: Robust Atlas-Based Segmentation of Highly Variable Anatomy: Left Atrium Segmentation

paper_content:

Automatic segmentation of the heart's left atrium offers great benefits for planning and outcome evaluation of atrial ablation procedures. However, the high anatomical variability of the left atrium presents significant challenges for atlas-guided segmentation. In this paper, we demonstrate an automatic method for left atrium segmentation using weighted voting label fusion and a variant of the demons registration algorithm adapted to handle images with different intensity distributions. We achieve accurate automatic segmentation that is robust to the high anatomical variations in the shape of the left atrium in a clinical dataset of MRA images.
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paper_title: Automatic segmentation of the left atrium from MRI images using salient feature and contour evolution

paper_content:

We propose an automatic approach for segmenting the left atrium from MRI images. In particular, the thoracic aorta is detected and used as a salient feature to find a seed region that lies inside the left atrium. A hybrid energy that combines robust statistics and localized region intensity information is employed to evolve active contours from the seed region to capture the whole left atrium. The experimental results demonstrate the accuracy and robustness of our approach.
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paper_title: Automatic left atrium segmentation by cutting the blood pool at narrowings

paper_content:

This paper presents a method to extract heart structures from CTA and MRA data sets, in particular the left atrium. First, the segmented blood pool is subdivided at narrowings in small components. Second, these basic components are merged automatically so that they represent the different heart structures. The resulting cutting surfaces have a relatively small diameter compared to the diameter of the neighboring heart chambers. Both steps are controlled by only one fixed parameter. The method is fast and allows interactive postprocessing by the user. Experiments on various data sets show the accuracy, robustness and repeatability of this approach.
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paper_title: Advanced level set segmentation of the right atrium in MR

paper_content:

Atrial fibrillation is a common heart arrhythmia, and can be effectively treated with ablation. Ablation planning ::: requires 3D models of the patient's left atrium (LA) and/or right atrium (RA), therefore an automatic segmentation ::: procedure to retrieve these models is desirable. In this study, we investigate the use of advanced level ::: set segmentation approaches to automatically segment RA in magnetic resonance angiographic (MRA) volume ::: images. Low contrast to noise ratio makes the boundary between the RA and the nearby structures nearly indistinguishable. ::: Therefore, pure data driven segmentation approaches such as watershed and ChanVese methods ::: are bound to fail. Incorporating training shapes through PCA modeling to constrain the segmentation is one ::: popular solution, and is also used in our segmentation framework. The shape parameters from PCA are optimized ::: with a global histogram based energy model. However, since the shape parameters span a much smaller ::: space, it can not capture fine details of the shape. Therefore, we employ a second refinement step after the shape ::: based segmentation stage, which follows closely the recent work of localized appearance model based techniques. ::: The local appearance model is established through a robust point tracking mechanism and is learned through ::: landmarks embedded on the surface of training shapes. The key contribution of our work is the combination ::: of a statistical shape prior and a localized appearance prior for level set segmentation of the right atrium from ::: MRA. We test this two step segmentation framework on porcine RA to verify the algorithm.
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paper_title: Fast, accurate, and fully automatic segmentation of the right ventricle in short-axis cardiac MRI

paper_content:

Abstract This paper presents a fully automatic method to segment the right ventricle (RV) from short-axis cardiac MRI. A combination of a novel window-constrained accumulator thresholding technique, binary difference of Gaussian (DoG) filters, optimal thresholding, and morphology are utilized to drive the segmentation. A priori segmentation window constraints are incorporated to guide and refine the process, as well as to ensure appropriate area confinement of the segmentation. Training and testing were performed using a combined 48 patient datasets supplied by the organizers of the MICCAI 2012 right ventricle segmentation challenge, allowing for unbiased evaluations and benchmark comparisons. Marked improvements in speed and accuracy over the top existing methods are demonstrated.
---
paper_title: Segmentation of Left Ventricle in Cardiac Cine MRI: An Automatic Image-Driven Method

paper_content:

This study investigates a fully automatic left ventricle segmentation method from cine short axis MR images. Advantages of this method include that it: 1) does not require manually drawn initial contours, trained statistical shape or gray-level appearance model; 2) provides not only endocardial and epicardial contours, but also papillary muscles and trabeculations' contours; 3) introduces a roundness measure that is fast and automatically locates the left ventricle; 4) simplifies the epicardial contour segmentation by mapping the pixels from Cartesian to approximately polar coordinates; and 5) applies a fast Fourier transform to smooth the endocardial and epicardial contours. Quantitative evaluation was performed on 41 subjects. The average perpendicular distance between manually drawn and automatically selected contours over all slices, all studies, and two phases (end-diastole and end-systole) was 1.40±1.18 mm for endocardial and 1.75±1.15 mm for epicardial contours. These results indicate a promising method for automatic segmentation of left ventricle for clinical use.
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Title: Cardiac MR Image Segmentation Techniques: An Overview

Section 1: INTRODUCTION
Description 1: This section introduces the significance of cardiac MRI, the need for accurate cardiac segmentation, and a high-level overview of the segmentation process.

Section 2: THE HUMAN HEART AND CARDIAC MRI
Description 2: This section describes the anatomy of the human heart, its imaging using MRI, and special considerations for capturing cardiac images.

Section 3: IMAGE SEGMENTATION TECHNIQUES
Description 3: This section provides an overview of various image segmentation techniques, categorized into different approaches such as region-based, clustering-based, edge-based, trained classifier-based, active contour techniques, and atlas-based methods.

Section 4: SEGMENTATION OF HUMAN HEART CHAMBERS
Description 4: This section discusses specific segmentation techniques applied to the different heart chambers, including the left ventricle, right ventricle, left atrium, and right atrium, highlighting challenges and methodologies used.

Section 5: SEGMENTATION ACCURACY
Description 5: This section explains the metrics used to measure the accuracy of cardiac MRI segmentation, specifically focusing on the Dice metric and the Hausdorff Distance.

Section 6: CONCLUSIVE SUMMARY
Description 6: This section provides a summary of the paper, emphasizing the importance of segmentation techniques, challenges across different heart chambers, and the predominance of active contour methods and atlas-based segmentation techniques.