Statistical shape models for 3D medical image segmentation: a review.

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A review of segmentation methods in short axis cardiac MR images

This comprehensive volume is an essential reference tool for professional and academic researchers in the filed of computer vision, image processing, and applied mathematics. Continuing rapid advances in image processing have been enhanced by the theoretical efforts of mathematicians and engineers. This marriage of mathematics and computer vision - computational vision - has resulted in a discrete approach to image processing that is more reliable when leveraging in practical tasks. This comprehensive volume provides a detailed discourse on the mathematical models used in computational vision from leading educators and active research experts in this field. Topical areas include: image reconstruction, segmentation and object extraction, shape modeling and registration, motion analysis and tracking, and 3D from images, geometry and reconstruction. The book also includes a study of applications in medical image analysis. Handbook of Mathematical Models in Computer Vision provides a graduate-level treatment of this subject as well as serving as a complete reference work for professionals.

Handbook of Mathematical Models in Computer Vision

Fully Convolutional Multi-scale Residual DenseNets for Cardiac Segmentation and Automated Cardiac Diagnosis using Ensemble of Classifiers

Cardiovascular magnetic resonance (CMR) has become a key imaging modality in clinical cardiology practice due to its unique capabilities for non-invasive imaging of the cardiac chambers and great vessels. A wide range of CMR sequences have been developed to assess various aspects of cardiac structure and function, and significant advances have also been made in terms of imaging quality and acquisition times. A lot of research has been dedicated to the development of global and regional quantitative CMR indices that help the distinction between health and pathology. The goal of this review paper is to discuss the structural and functional CMR indices that have been proposed thus far for clinical assessment of the cardiac chambers. We include indices definitions, the requirements for the calculations, exemplar applications in cardiovascular diseases, and the corresponding normal ranges. Furthermore, we review the most recent state-of-the art techniques for the automatic segmentation of the cardiac boundaries, which are necessary for the calculation of the CMR indices. Finally, we provide a detailed discussion of the existing literature and of the future challenges that need to be addressed to enable a more robust and comprehensive assessment of the cardiac chambers in clinical practice.

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A review of heart chamber segmentation for structural and functional analysis using cardiac magnetic resonance imaging

In this article, we propose a semiautomatic method for time-continuous contour detection in all phases of the cardiac cycle in magnetic resonance sequences. The method is based on multidimensional dynamic programming. After shape parameterization, cost hypercubes are filled with image-feature derived cost function values. Using multidimensional dynamic programming, an optimal path is sought through the sequence of hypercubes. Constraints can be imposed by setting limits to the parameter changes between subsequent hypercubes. Quantitative evaluation was performed on 20 subjects. Average border positioning error over all slices, all phases and all studies, was 1.77 ± 0.57 mm for epicardial and 1.86 ± 0.59 mm for endocardial contours. The average error in end-diastolic and end-systolic volumes over all studies was small: 4.24 ± 4.62 mL and −4.36 ± 4.26 mL, respectively. The average error in ejection fraction was 4.82 ± 3.01%. The reported results compare favorable to the best-reported results in recent literature, underlining the potential of this method for application in daily clinical practice.

Time continuous tracking and segmentation of cardiovascular magnetic resonance images using multidimensional dynamic programming.

Statistical shape models generally use Principal Component Analysis (PCA) to describe the main directions of shape variation in a training set of example shapes. However, PCA assumes a number of restrictions on the data that do not always hold. In this paper we explore the use of an alternative shape decomposition, Independent Component Analysis (ICA), which does not assume a Gaussian distribution of the input data. Several different methods for performing ICA are available. Three most frequently used methods were tested in order to evaluate their effect on the resulting vectors.
In statistical shape models, generally not all the eigenvectors that result from the PCA are used. Vectors de-scribing noise are discarded to obtain a compact description of the data set. The selection of these vectors is based on the natural ordering of the vectors according to the variance in that direction which is inherent to PCA. With ICA, how-ever, there is no natural ordering of the vectors. Four methods for sorting the ICA vectors are investigated.
The different ICA-methods yielded highly similar yet not identical results. Vectors obtained with ICA showed localized shape variations, whereas eigenvectors obtained with PCA show global shape variations. From the results of the ordering methods can be seen that PCA is better suited for dimensionality reduction. Of the ordering methods that were tested, the best results were obtained with the ordering according to the locality of the shape variations.

Independent component analysis in statistical shape models

Statistical shape models generally use Principal Component Analysis (PCA) to describe the main directions of shape variation in a training set of example shapes. However, PCA has the restriction that the input data must be drawn from a Gaussian distribution and is only able to describe global shape variations. In this paper we evaluate the use of an alternative shape decomposition, Independent Component Analysis (ICA), for two reasons. ICA does not require a Gaussian distribution of the input data and is able to describe localized shape variations. With ICA however, the resulting vectors are not ordered, therefore a method for ordering the Independent Components is presented in this paper. To evaluate ICA-based Active Appearance Models (AAMs), 10 leave-15-out models were trained on a set of 150 short-axis cardiac MR Images with PCA-based as well as ICA-based AAMs. The median values for the average and maximal point-to-point distances between the expert drawn and automatically segmented contours for the PCA-based AAM were 2.95 and 8.39 pixels. For the ICA-based AAM these distances were 1.86 and 5.01 pixels respectively. From this, we conclude that the use of ICA results in a substantial improvement in border localization accuracy over a PCA-based model.

/pdf/ica-vs-pca-active-appearance-models-application-to-cardiac-2gnr4cbj6q.pdf

ICA vs. PCA Active Appearance Models: Application to Cardiac MR Segmentation

This paper describes a multi-view Active Appearance Model (AAM) for coherent segmentation of multiple cardiac views. Cootes' AAM framework was adapted by considering shapes and intensities from multiple views as single shape and intensity samples, while eliminating trivial difference in object pose in different views. This way, the coherence in organ shape and intensities between different views is modeled and utilized during image search. In a leave-one-out validation study on a combination of four-chamber, two-chamber and short-axis cardiac MR views from 29 patients, low border positioning errors were found comparing manual and computer detected borders, whereas no statistically significant difference in contour area was present. The presented validation shows that the method combines a high robustness with clinically acceptable accuracy, and therefore is a promising tool to further automate the integral quantitative analysis of cardiac MR patient examinations.

Multi-view active appearance models for consistent segmentation of multiple standard views: application to long- and short-axis cardiac MR images

Rationale and Objectives: Long-axis cardiac magnetic resonance (MR) views enable a rapid, online evaluation of cardiac function from only 2 views. In this article, we aimed to evaluate a model-based method for the simultaneous detection of 2- and 4-chamber endocardial and epicardial contours in end-diastolic and end-systolic phases of MR images. Methods: We introduce multiview Active Appearance Models for the automated segmentation of long-axis cardiac MR images of the left ventricle. Two modes of initialization were used to test the accuracy of the model with minimal user interaction and the best-obtainable accuracy with this model. The segmentation was initialized by annotating 2 points in the base and one in the apex. We tested the method's performance by comparing the point-to-curve errors, ejection fractions, and biplane area-length volumes calculated with the automatically detected contours to those calculated from contours that were annotated manually by experts. Leave-one-out experiments were performed with 2- and 4-chamber long axis MR images of 59 subjects in end-diastolic and end-systolic phases. Results: When initializing in all 4 frames, 97% of the segmentations were successful, and the standard deviation in the volume-errors with respect to the average manually identified volume was 9.0% for the end-diastolic volumes and 15% for the end-systolic volumes. When the method was initialized in the end-systolic frames only, 92% of the segmentations were successful, and the standard deviation in the errors in the volumes with respect to the average manually identified volume was 13.3% for the end-diastolic volumes and 16.7% for the end-systolic volumes. Bland–Altman plots showed that the errors were distributed randomly around 0, and by using a paired t test comparing manual and computer-determined volumes, we were able to detect that the volume differences were not significant. Simultaneous detection of the endocardial and epicardial contours in 2- and 4-chamber views and end-diastolic and end-systolic phases for one subject takes approximately 3 seconds. Conclusions: The accuracy of the reported method is comparable with the interobserver variability for initialization in all frames and slightly worse than the interobserver variability with initialization in the end-systolic frames only. However, in both cases the errors were not significant. Initialization in end-systolic frames only leads to a statistically insignificantly lower model accuracy; however, it requires only half the user interaction. Therefore, we can conclude that this method enables rapid analysis of the cardiac left ventricular function with little user interaction.

Mehmet Üzümcü

Papers

Time continuous tracking and segmentation of cardiovascular magnetic resonance images using multidimensional dynamic programming.

Independent component analysis in statistical shape models

ICA vs. PCA Active Appearance Models: Application to Cardiac MR Segmentation

Multi-view active appearance models for consistent segmentation of multiple standard views: application to long- and short-axis cardiac MR images

Multiview active appearance models for simultaneous segmentation of cardiac 2- and 4-chamber long-axis magnetic resonance images