Automated volume measurements in echocardiography by utilizing expert knowledge

Left ventricular (LV) volumes and ejection fraction (EF) are important parameters for diagnosis, prognosis, and treatment planning in patients with heart disease. These parameters are commonly measured by manual tracing in echocardiographic images, a procedure that is time consuming, prone to inter- and intra-observer variability, and require highly trained operators. This is particularly the case in three-dimensional (3D) echocardiography, where the increased amount of data makes manual tracing impractical. Automated methods for measuring LV volumes and EF can therefore improve efficiency and accuracy of echocardiographic examinations, giving better diagnosis at a lower cost. The main goal of this thesis was to improve the efficiency and quality of cardiac measurements. More specifically, the goal was to develop rapid and accurate methods that utilize expert knowledge for automated evaluation of cardiac function in echocardiography. The thesis presents several methods for automated volume and EF measurements in echocardiographic data. For two-dimensional (2D) echocardiography, an atlas based segmentation algorithm is presented in paper A. This method utilizes manually traced endocardial contours in a validated case database to control a snake optimized by dynamic programming. The challenge with this approach is to find the most optimal case in the database. More promising results are achieved in triplane echocardiography using a multiview and multi-frame extension to the active appearance model (AAM) framework, as demonstrated in paper B. The AAM generalizes better to new patient data and is based on more robust optimization schemes than the atlas-based method. In triplane images, the results of the AAM algorithm may be improved further by integrating a snake algorithm into the AAM framework and by constraining the AAM to manually defined landmarks, and this is shown in paper C. For 3D echocardiograms, a clinical semi-automated volume measurement tool with expert selected points is validated in paper D. This tool compares favorably to a reference measurement tool, with good agreement in measured volumes, and with a significantly lower analysis time. Finally, in paper E, fully automated real-time segmentation in 3D echocardiography is demonstrated using a 3D active shape model (ASM) of the left ventricle in a Kalman filter framework. The main advantage of this approach is its processing performance, allowing for real-time volume and EF estimates. Statistical models such as AAMs and ASMs provide elegant frameworks for incorporating expert knowledge into segmentation algorithms. Expert knowledge can also be utilized directly through manual input to semi-automated methods, allowing for manual initialization and correction of automatically determined volumes. The latter technique is particularly suitable for clinical routine examinations, while the fully automated 3D ASM method can extend the use of echocardiography to new clinical areas such as automated patient monitoring. In this thesis, different methods for utilizing expert knowledge in automated segmentation algorithms for echocardiography have been developed and evaluated. Particularly in 3D echocardiography, these contributions are expected to improve efficiency and quality of cardiac measurements

Semi-automated quantification of left ventricular volumes and ejection fraction by real-time three-dimensional echocardiography

<p>Abstract</p> <p>Background</p> <p>Recent studies have shown that real-time three-dimensional (3D) echocardiography (RT3DE) gives more accurate and reproducible left ventricular (LV) volume and ejection fraction (EF) measurements than traditional two-dimensional methods. A new semi-automated tool (4DLVQ) for volume measurements in RT3DE has been developed. We sought to evaluate the accuracy and repeatability of this method compared to a 3D echo standard.</p> <p>Methods</p> <p>LV end-diastolic volumes (EDV), end-systolic volumes (ESV), and EF measured using 4DLVQ were compared with a commercially available semi-automated analysis tool (TomTec 4D LV-Analysis ver. 2.2) in 35 patients. Repeated measurements were performed to investigate inter- and intra-observer variability.</p> <p>Results</p> <p>Average analysis time of the new tool was 141s, significantly shorter than 261s using TomTec (<it>p </it>< 0.001). Bland Altman analysis revealed high agreement of measured EDV, ESV, and EF compared to TomTec (<it>p </it>= <it>NS</it>), with bias and 95% limits of agreement of 2.1 ± 21 ml, -0.88 ± 17 ml, and 1.6 ± 11% for EDV, ESV, and EF respectively. Intra-observer variability of 4DLVQ vs. TomTec was 7.5 ± 6.2 ml vs. 7.7 ± 7.3 ml for EDV, 5.5 ± 5.6 ml vs. 5.0 ± 5.9 ml for ESV, and 3.0 ± 2.7% vs. 2.1 ± 2.0% for EF (<it>p </it>= <it>NS</it>). The inter-observer variability of 4DLVQ vs. TomTec was 9.0 ± 5.9 ml vs. 17 ± 6.3 ml for EDV (<it>p </it>< 0.05), 5.0 ± 3.6 ml vs. 12 ± 7.7 ml for ESV (<it>p </it>< 0.05), and 2.7 ± 2.8% vs. 3.0 ± 2.1% for EF (<it>p </it>= <it>NS</it>).</p> <p>Conclusion</p> <p>In conclusion, the new analysis tool gives rapid and reproducible measurements of LV volumes and EF, with good agreement compared to another RT3DE volume quantification tool.</p

Left Ventricular Border Tracking Using Cardiac Motion Models and Optical Flow

The use of automated methods is becoming increasingly important for assessing cardiac function quantitatively and objectively. In this study, we propose a method for tracking three-dimensional (3-D) left ventricular contours. The method consists of a local optical flow tracker and a global tracker, which uses a statistical model of cardiac motion in an optical-flow formulation. We propose a combination of local and global trackers using gradient-based weights. The algorithm was tested on 35 echocardiographic sequences, with good results (surface error: 1.35 ± 0.46 mm, absolute volume error: 5.4 ± 4.8 mL). This demonstrates the method’s potential in automated tracking in clinical quality echocardiograms, facilitating the quantitative and objective assessment of cardiac functio

Left-ventricular epi- and endocardium extraction from 3D ultrasound images using an automatically constructed 3D ASM

© 2014 Taylor & Francis.In this paper, we propose an automatic method for constructing an active shape model (ASM) to segment the complete cardiac left ventricle in 3D ultrasound (3DUS) images, which avoids costly manual landmarking. The automatic construction of the ASM has already been addressed in the literature; however, the direct application of these methods to 3DUS is hampered by a high level of noise and artefacts. Therefore, we propose to construct the ASM by fusing the multidetector computed tomography data, to learn the shape, with the artificially generated 3DUS, in order to learn the neighbourhood of the boundaries. Our artificial images were generated by two approaches: a faster one that does not take into account the geometry of the transducer, and a more comprehensive one, implemented in Field II toolbox. The segmentation accuracy of our ASM was evaluated on 20 patients with left-ventricular asynchrony, demonstrating plausibility of the approach

Atlas construction and image analysis using statistical cardiac models

International audienceThis paper presents a brief overview of current trends in the construction of population and multi-modal heart atlases in our group and their application to atlas-based cardiac image analysis. The technical challenges around the construction of these atlases are organized around two main axes: groupwise image registration of anatomical, motion and fiber images and construction of statistical shape models. Application-wise, this paper focuses on the extraction of atlas-based biomarkers for the detection of local shape or motion abnormalities, addressing several cardiac applications where the extracted information is used to study and grade different pathologies. The paper is concluded with a discussion about the role of statistical atlases in the integration of multiple information sources and the potential this can bring to in-silico simulations

Automated analysis of 3D echocardiography

In this thesis we aim at automating the analysis of 3D echocardiography, mainly targeting the functional analysis of the left ventricle. Manual analysis of these data is cumbersome, time-consuming and is associated with inter-observer and inter-institutional variability. Methods for reconstruction of 3D echocardiographic images from fast rotating ultrasound transducers is presented and methods for analysis of 3D echocardiography in general, using tracking, detection and model-based segmentation techniques to ultimately fully automatically segment the left ventricle for functional analysis. We show that reliable quantification of left ventricular volume and mitral valve displacement can be achieved using the presented techniques.SenterNovem (IOP Beeldverwerking, grant IBVC02003), Dutch Technology Foundation STW (grant 06666)UBL - phd migration 201

Model-driven segmentation of X-ray left ventricular angiograms

X-ray left ventricular (LV) angiography is an important imaging modality to assess cardiac function. Using a contrast fluid a 2D projection of the heart is obtained. In current clinical practice cardiac function is analyzed by drawing two contours manually: one in the end diastolic (ED) phase and one in the end systolic (ES) phase. From the contours the LV volumes in these phases are calculated and the patient__s ejection fraction is assessed. Drawing these contours manually is a cumbersome and time-consuming task for a medical doctor. Furthermore, manual drawing introduces inter- and intra-observer variabilities. The focus of the research presented in this thesis was to automate the process of contour drawing in X-ray LV angiography. The developed method is based on Active Appearance Models. These statistical models, in which the cardiac shape and the cardiac appearance are modeled, have proven to be able to mimic the drawing behavior of an expert cardiologist. The clinical parameters, as determined by the automated method, showed a similar degree of accuracy as when determined by an expert. Furthermore, the required time for patient analysis was reduced considerably and the inter- and intra-observer variabilities were structurally decreased.UBL - phd migration 201

The Probabilistic Active Shape Model: From Model Construction to Flexible Medical Image Segmentation

Automatic processing of three-dimensional image data acquired with computed tomography or magnetic resonance imaging plays an increasingly important role in medicine. For example, the automatic segmentation of anatomical structures in tomographic images allows to generate three-dimensional visualizations of a patient’s anatomy and thereby supports surgeons during planning of various kinds of surgeries. Because organs in medical images often exhibit a low contrast to adjacent structures, and because the image quality may be hampered by noise or other image acquisition artifacts, the development of segmentation algorithms that are both robust and accurate is very challenging. In order to increase the robustness, the use of model-based algorithms is mandatory, as for example algorithms that incorporate prior knowledge about an organ’s shape into the segmentation process. Recent research has proven that Statistical Shape Models are especially appropriate for robust medical image segmentation. In these models, the typical shape of an organ is learned from a set of training examples. However, Statistical Shape Models have two major disadvantages: The construction of the models is relatively difficult, and the models are often used too restrictively, such that the resulting segmentation does not delineate the organ exactly. This thesis addresses both problems: The first part of the thesis introduces new methods for establishing correspondence between training shapes, which is a necessary prerequisite for shape model learning. The developed methods include consistent parameterization algorithms for organs with spherical and genus 1 topology, as well as a nonrigid mesh registration algorithm for shapes with arbitrary topology. The second part of the thesis presents a new shape model-based segmentation algorithm that allows for an accurate delineation of organs. In contrast to existing approaches, it is possible to integrate not only linear shape models into the algorithm, but also nonlinear shape models, which allow for a more specific description of an organ’s shape variation. The proposed segmentation algorithm is evaluated in three applications to medical image data: Liver and vertebra segmentation in contrast-enhanced computed tomography scans, and prostate segmentation in magnetic resonance images