1.1 Motivation
Cardiovascular disease (CVD), an umbrella term for a myriad of conditions affecting the heart and the blood vessels, is the top-occurring disease in many places in the world. In 2008 alone, this deadly disease claimed an estimated 17.3 million lives, according to the World Health Organization, making it the single largest cause of death worldwide [1].
Aside from high mortality rate, the prevalence of CVD continuously imposes a heavy burden on the economy and public healthcare system of those affected countries. In fact, coronary heart disease, one common type of CVD, costs the United States as much as
$108.9 billion each year [2]. Experts and specialists thus call for a constant effort to reduce mortality rate and the cost involved through proper prevention, identification, and monitoring following the diagnosis of the disease. While recent decades have seen a decrease of mortality rate of CVD in industrialized countries, its surge in the developing countries makes CVD remain the deadliest disease in the world [3].
Diagnosing subject’s cardiac health in the early stages is therefore a key step to facilitate further treatments and increase the chance of survival, which requires, among others, quantitative assessment of the cardiac health. These include measuring myocardial mass (MM), peak ejection rate (PER), peak filling rate (PFR), ejection fraction (EF), stroke volume (SV) and its associated cardiac output (Q). In particular, cardiovascular diseases are often related with deviation of cardiac output Q, derived from segmenting left ventricle (LV) in cardiac magnetic resonance (MR) images over a cardiac cycle.
As the cardiac cine MR scan of one patient consists of a few dozen to several hundred individual images to work on, segmenting the ventricle by trained operator consumes a considerable amount of time and manpower. Another drawback of manual segmentation
is that it is prone to operator bias due to complex inner myocardial structures such as the papillary muscles and trabeculae carneae [4]. Therefore, automation of this task highly appeals to clinicians as it is able to eliminate such operator bias and expedite the evaluation process. At this moment, however, the popular commercial software MASS provides merely moderately usable segmentation results and the resulting cardiac parameters are less reliable [5], which would require more operator intervention to correct failed segmentations. Nevertheless, because of its popularity and ability of full integration into MRI scanning workflow, it has been compared against as a baseline algorithm by many previous works [4], [6].
Segmenting the ventricles in cardiac magnetic resonance (CMR) images by automation is not trivial, even in noiseless cases. Challenges are: 1) substantial shape variation across slices and timeframes, 2) inter-subject variation resulted from different pathologies, and 3) various distortions of the MR image itself, such as partial volume effect, field inhomogeneity, and low contrast between myocardium and lungs. Moreover, many previous works did not achieve full automation; some require a training set prior to segmentation, others require a coarse contour as initialization. Despite ongoing research on the CMR image segmentation, it is still acknowledged as an open problem by a recent survey [7].
1.2 Research Goal
As there are many previous works on automation of left ventricle segmentation that still require some degree of human intervention before and during the segmentation process (such as constructing training sets and priors, which we will point out in Section 1.4), we therefore look to lessen the those restrictions and aiming for maximum automation.
Despite many previous methods attempting to ensure consistency across time frames and slices of the CMR 4D volume, there are however only a few, for example the work by Lee et al. and Jolly et al., that attempt to provide a robust solution with automation in mind. Therefore, this thesis dedicates the goal to automation and precision in segmenting left ventricle in cardiac cine MR images. The main objectives are:
• Full automation: it requires no manual intervention before and during the segmentation process.
• Precise segmentation: despite aiming for full automation, the segmentation precision is not to be sacrificed. More so, the result must be up to par with the state-of-the-art in order to have a meaningful impact in this field.
• Eliminate operator bias: by providing robust, reliable segmentation algorithm for clinical use, intra- and inter-observer variability are resulted from hand-drawing left ventricle myocardium contours can be eliminated
• Estimate crucial cardiac parameters: these parameters are indispensable in assessing cardiac functions of the patient, including ejection fraction, peak ejection rate, etc.
For the purpose of maximum automation, we have developed an image-driven approach. The operator is not required to construct any prior model or trace left ventricle contour in order to initialize the algorithm. We argue that a general prior model is insufficient to describe all possible cases which often leads to inaccurate segmentation, shows the literature survey. In fact, we believe minimal assumption is the key to the proposed method’s superiority and robustness against various pathologies.
1.3 Contribution
We have employed novel cost initialization scheme for the cost-volume-filtering (CVF) based image segmentation. Together with novel myocardial contour processing framework, the proposed computational framework is able to overcome the segmentation difficulty caused by PMTC (papillary muscle and trabeculae carneae) tissues that prevent previous methods from achieving high segmentation accuracy.
1.4 Literature Survey
Segmentation of cardiac magnetic resonance (CMR) images has been an area of active research for the past two decades; it has accumulated a considerable amount of work so far. For starters, Petitjean et al. [7] compiled a comprehensive list of recent works up until 2011, which is a good introduction for newcomers in this field. In this section, we limit our scope of the literature survey and focus on the segmentation of the left ventricle (LV) in short-axis view MR images.
A class of approach using region- or edge-based methods often involves the use of binary thresholding, region growing, and edge-detection, and mathematical morphology [4], [8]–[14]. Lu et al. [11] identifies the LV cavity among candidates according to the roundness metric of each connected component. Then, for extracting the LV endocardium, Otsu’s optimal thresholding [15] and convex hull fitting are applied to the selected LV cavity. As for the epicardium, they use region growing and morphological operation refinements to segment the muscle wall. In the last stage of the algorithm, both contours are smoothed by taking higher frequency components out of each contour’s respective Fourier descriptor. The drawback is the algorithm failed in image slices with left ventricular outflow tract (LVOT); they later extended their work to address this issue [6].
Huang et al. [10], [12] followed a similar processing flows as Lu’s, therefore suffered from similar failure in the presence of LVOT. However, their work differed in that gradient information is taken into consideration when delineating the endocardium. A performance improvement over Lu’s previous work was reported. Lee et al. [4] used a hybrid of region-growing and active contour driven by modified gradient field to extract endocardium and epicardium respectively, and the results show high correlation between manual and computed cardiac functions.
Another class of approach can be listed under the category called deformable models, which involves the use of active shape and appearance model (ASM/AAM) and level set methods. In principle, a shape or a contour, which usually starts from an initial contour annotated by the operator, iteratively deforms, where it’s deformation is driven by both the external force (such as gradient or local intensity distribution) and internal force (such as smoothness constraint) [16]–[19]. These approaches have the additional flexibility to incorporate shape priors or motion models [20]–[23]. For instance, Lynch et al. [20]
encoded the parametric volume-over-time model into the evolution of level set. It achieved only moderate results measured by correlation, possibly due to its emphasis on temporal constraint, thus overlooking the information from the images themselves.
Schaerer et al. [22] also sought to incorporate motion model into the processing flow, but suffered from similar sub-par segmentation accuracy.
Aside from the flexible deformable models paradigm, many other approaches are attempted as well. Jolly [24] proposed a minimal surface approach. Candidate contours are first computed from the average of a particular slice across all time frames registered to a particular phase. Those candidate contours are transformed back to respective time frames, and finally propagated to other slices. Minimal surface is computed using these back-transformed candidate contours as boundary constraints. It is fully automatic, offers
temporal and inter-slice consistency, but comes at the price of less segmentation accuracy.
Lorenzo-Valdés et al. [25] used probabilistic atlas-based approach in which a probability prior for each tissue class (e.g., myocardium, LV cavity) is constructed by hand, which is very laborious—they reported to have manually segmented 14 healthy volunteers’ full MR 4D volume, which translates to segmenting hundreds of MR images. And given that they only train the model using healthy case, the reliability is overshadowed by unforeseen pathologies because the performance relies on the training set. On the other hand, training a wide set of models in an attempt for more accuracy seems to negate the benefit of automation in the first place.
1.5 Thesis Outline
Chapter I highlights the motivation and contribution of this study, as well as an overview of previous studies in CMR segmentation. In Chapter II, we briefly review the anatomy of the heart before guiding the reader through the cardiac MR images. In Chapter III, we introduce the proposed computational framework for segmenting the left ventricle in short-axis view cardiac cine MR images. Experimental results and as well as detailed discussions regarding the performance of proposed method are shown in Chapter IV. And finally, Chapter V marks the conclusion and future prospect of this work.