Computational Methods for Segmentation of Multi-Modal Multi-Dimensional Cardiac Images

Segmentation of the heart structures helps compute the cardiac contractile function quantified via the systolic and diastolic volumes, ejection fraction, and myocardial mass, representing a reliable diagnostic value. Similarly, quantification of the myocardial mechanics throughout the cardiac cycle, analysis of the activation patterns in the heart via electrocardiography (ECG) signals, serve as good cardiac diagnosis indicators. Furthermore, high quality anatomical models of the heart can be used in planning and guidance of minimally invasive interventions under the assistance of image guidance. The most crucial step for the above mentioned applications is to segment the ventricles and myocardium from the acquired cardiac image data. Although the manual delineation of the heart structures is deemed as the gold-standard approach, it requires significant time and effort, and is highly susceptible to inter- and intra-observer variability. These limitations suggest a need for fast, robust, and accurate semi- or fully-automatic segmentation algorithms. However, the complex motion and anatomy of the heart, indistinct borders due to blood flow, the presence of trabeculations, intensity inhomogeneity, and various other imaging artifacts, makes the segmentation task challenging. In this work, we present and evaluate segmentation algorithms for multi-modal, multi-dimensional cardiac image datasets. Firstly, we segment the left ventricle (LV) blood-pool from a tri-plane 2D+time trans-esophageal (TEE) ultrasound acquisition using local phase based filtering and graph-cut technique, propagate the segmentation throughout the cardiac cycle using non-rigid registration-based motion extraction, and reconstruct the 3D LV geometry. Secondly, we segment the LV blood-pool and myocardium from an open-source 4D cardiac cine Magnetic Resonance Imaging (MRI) dataset by incorporating average atlas based shape constraint into the graph-cut framework and iterative segmentation refinement. The developed fast and robust framework is further extended to perform right ventricle (RV) blood-pool segmentation from a different open-source 4D cardiac cine MRI dataset. Next, we employ convolutional neural network based multi-task learning framework to segment the myocardium and regress its area, simultaneously, and show that segmentation based computation of the myocardial area is significantly better than that regressed directly from the network, while also being more interpretable. Finally, we impose a weak shape constraint via multi-task learning framework in a fully convolutional network and show improved segmentation performance for LV, RV and myocardium across healthy and pathological cases, as well as, in the challenging apical and basal slices in two open-source 4D cardiac cine MRI datasets. We demonstrate the accuracy and robustness of the proposed segmentation methods by comparing the obtained results against the provided gold-standard manual segmentations, as well as with other competing segmentation methods

Learning the dynamics and time-recursive boundary detection of deformable objects

We propose a principled framework for recursively segmenting deformable objects across a sequence of frames. We demonstrate the usefulness of this method on left ventricular segmentation across a cardiac cycle. The approach involves a technique for learning the system dynamics together with methods of particle-based smoothing as well as non-parametric belief propagation on a loopy graphical model capturing the temporal periodicity of the heart. The dynamic system state is a low-dimensional representation of the boundary, and the boundary estimation involves incorporating curve evolution into recursive state estimation. By formulating the problem as one of state estimation, the segmentation at each particular time is based not only on the data observed at that instant, but also on predictions based on past and future boundary estimates. Although the paper focuses on left ventricle segmentation, the method generalizes to temporally segmenting any deformable object

Optimization-based interactive segmentation interface for multiregion problems.

Interactive segmentation is becoming of increasing interest to the medical imaging community in that it combines the positive aspects of both manual and automated segmentation. However, general-purpose tools have been lacking in terms of segmenting multiple regions simultaneously with a high degree of coupling between groups of labels. Hierarchical max-flow segmentation has taken advantage of this coupling for individual applications, but until recently, these algorithms were constrained to a particular hierarchy and could not be considered general-purpose. In a generalized form, the hierarchy for any given segmentation problem is specified in run-time, allowing different hierarchies to be quickly explored. We present an interactive segmentation interface, which uses generalized hierarchical max-flow for optimization-based multiregion segmentation guided by user-defined seeds. Applications in cardiac and neonatal brain segmentation are given as example applications of its generality

The anthropometric, environmental and genetic determinants of right ventricular structure and function

BACKGROUND Measures of right ventricular (RV) structure and function have significant prognostic value. The right ventricle is currently assessed by global measures, or point surrogates, which are insensitive to regional and directional changes. We aim to create a high-resolution three-dimensional RV model to improve understanding of its structural and functional determinants. These may be particularly of interest in pulmonary hypertension (PH), a condition in which RV function and outcome are strongly linked. PURPOSE To investigate the feasibility and additional benefit of applying three-dimensional phenotyping and contemporary statistical and genetic approaches to large patient populations. METHODS Healthy subjects and incident PH patients were prospectively recruited. Using a semi-automated atlas-based segmentation algorithm, 3D models characterising RV wall position and displacement were developed, validated and compared with anthropometric, physiological and genetic influences. Statistical techniques were adapted from other high-dimensional approaches to deal with the problems of multiple testing, contiguity, sparsity and computational burden. RESULTS 1527 healthy subjects successfully completed high-resolution 3D CMR and automated segmentation. Of these, 927 subjects underwent next-generation sequencing of the sarcomeric gene titin and 947 subjects completed genotyping of common variants for genome-wide association study. 405 incident PH patients were recruited, of whom 256 completed phenotyping. 3D modelling demonstrated significant reductions in sample size compared to two-dimensional approaches. 3D analysis demonstrated that RV basal-freewall function reflects global functional changes most accurately and that a similar region in PH patients provides stronger survival prediction than all anthropometric, haemodynamic and functional markers. Vascular stiffness, titin truncating variants and common variants may also contribute to changes in RV structure and function. CONCLUSIONS High-resolution phenotyping coupled with computational analysis methods can improve insights into the determinants of RV structure and function in both healthy subjects and PH patients. Large, population-based approaches offer physiological insights relevant to clinical care in selected patient groups.Open Acces

Topology polymorphism graph for lung tumor segmentation in PET-CT images

Accurate lung tumor segmentation is problematic when the tumor boundary or edge, which reflects the advancing edge of the tumor, is difficult to discern on chest CT or PET. We propose a ‘topo-poly’ graph model to improve identification of the tumor extent. Our model incorporates an intensity graph and a topology graph. The intensity graph provides the joint PET-CT foreground similarity to differentiate the tumor from surrounding tissues. The topology graph is defined on the basis of contour tree to reflect the inclusion and exclusion relationship of regions. By taking into account different topology relations, the edges in our model exhibit topological polymorphism. These polymorphic edges in turn affect the energy cost when crossing different topology regions under a random walk framework, and hence contribute to appropriate tumor delineation. We validated our method on 40 patients with non-small cell lung cancer where the tumors were manually delineated by a clinical expert. The studies were separated into an ‘isolated’ group (n = 20) where the lung tumor was located in the lung parenchyma and away from associated structures / tissues in the thorax and a ‘complex’ group (n = 20) where the tumor abutted / involved a variety of adjacent structures and had heterogeneous FDG uptake. The methods were validated using Dice’s similarity coefficient (DSC) to measure the spatial volume overlap and Hausdorff distance (HD) to compare shape similarity calculated as the maximum surface distance between the segmentation results and the manual delineations. Our method achieved an average DSC of 0.881  ±  0.046 and HD of 5.311  ±  3.022 mm for the isolated cases and DSC of 0.870  ±  0.038 and HD of 9.370  ±  3.169 mm for the complex cases. Student’s t-test showed that our model outperformed the other methods (p-values <0.05)

Exploiting Temporal Image Information in Minimally Invasive Surgery

Minimally invasive procedures rely on medical imaging instead of the surgeons direct vision. While preoperative images can be used for surgical planning and navigation, once the surgeon arrives at the target site real-time intraoperative imaging is needed. However, acquiring and interpreting these images can be challenging and much of the rich temporal information present in these images is not visible. The goal of this thesis is to improve image guidance for minimally invasive surgery in two main areas. First, by showing how high-quality ultrasound video can be obtained by integrating an ultrasound transducer directly into delivery devices for beating heart valve surgery. Secondly, by extracting hidden temporal information through video processing methods to help the surgeon localize important anatomical structures. Prototypes of delivery tools, with integrated ultrasound imaging, were developed for both transcatheter aortic valve implantation and mitral valve repair. These tools provided an on-site view that shows the tool-tissue interactions during valve repair. Additionally, augmented reality environments were used to add more anatomical context that aids in navigation and in interpreting the on-site video. Other procedures can be improved by extracting hidden temporal information from the intraoperative video. In ultrasound guided epidural injections, dural pulsation provides a cue in finding a clear trajectory to the epidural space. By processing the video using extended Kalman filtering, subtle pulsations were automatically detected and visualized in real-time. A statistical framework for analyzing periodicity was developed based on dynamic linear modelling. In addition to detecting dural pulsation in lumbar spine ultrasound, this approach was used to image tissue perfusion in natural video and generate ventilation maps from free-breathing magnetic resonance imaging. A second statistical method, based on spectral analysis of pixel intensity values, allowed blood flow to be detected directly from high-frequency B-mode ultrasound video. Finally, pulsatile cues in endoscopic video were enhanced through Eulerian video magnification to help localize critical vasculature. This approach shows particular promise in identifying the basilar artery in endoscopic third ventriculostomy and the prostatic artery in nerve-sparing prostatectomy. A real-time implementation was developed which processed full-resolution stereoscopic video on the da Vinci Surgical System

Automated deep phenotyping of the cardiovascular system using magnetic resonance imaging

Across a lifetime, the cardiovascular system must adapt to a great range of demands from the body. The individual changes in the cardiovascular system that occur in response to loading conditions are influenced by genetic susceptibility, and the pattern and extent of these changes have prognostic value. Brachial blood pressure (BP) and left ventricular ejection fraction (LVEF) are important biomarkers that capture this response, and their measurements are made at high resolution. Relatively, clinical analysis is crude, and may result in lost information and the introduction of noise. Digital information storage enables efficient extraction of information from a dataset, and this strategy may provide more precise and deeper measures to breakdown current phenotypes into their component parts. The aim of this thesis was to develop automated analysis of cardiovascular magnetic resonance (CMR) imaging for more detailed phenotyping, and apply these techniques for new biological insights into the cardiovascular response to different loading conditions. I therefore tested the feasibility and clinical utility of computational approaches for image and waveform analysis, recruiting and acquiring additional patient cohorts where necessary, and then applied these approaches prospectively to participants before and after six-months of exercise training for a first-time marathon. First, a multi-centre, multi-vendor, multi-field strength, multi-disease CMR resource of 110 patients undergoing repeat imaging in a short time-frame was assembled. The resource was used to assess whether automated analysis of LV structure and function is feasible on real-world data, and if it can improve upon human precision. This showed that clinicians can be confident in detecting a 9% change in EF or a 20g change in LV mass. This will be difficult to improve by clinicians because the greatest source of human error was attributable to the observer rather than modifiable factors. Having understood these errors, a convolutional neural network was trained on separate multi-centre data for automated analysis and was successfully generalizable to the real-world CMR data. Precision was similar to human analysis, and performance was 186 times faster. This real-world benchmarking resource has been made freely available (thevolumesresource.com). Precise automated segmentations were then used as a platform to delve further into the LV phenotype. Global LVEFs measured from CMR imaging in 116 patients with severe aortic stenosis were broken down into ~10 million regional measurements of structure and function, represented by computational three-dimensional LV models for each individual. A cardiac atlas approach was used to compile, label, segment and represent these data. Models were compared with healthy matched controls, and co-registered with follow-up one year after aortic valve replacement (AVR). This showed that there is a tendency to asymmetric septal hypertrophy in all patients with severe aortic stenosis (AS), rather than a characteristic specific to predisposed patients. This response to AS was more unfavourable in males than females (associated with higher NT-proBNP, and lower blood pressure), but was more modifiable with AVR. This was not detected using conventional analysis. Because cardiac function is coupled with the vasculature, a novel integrated assessment of the cardiovascular system was developed. Wave intensity theory was used to combine central blood pressure and CMR aortic blood flow-velocity waveforms to represent the interaction of the heart with the vessels in terms of traveling energy waves. This was performed and then validated in 206 individuals (the largest cohort to date), demonstrating inefficient ventriculo-arterial coupling in female sex and healthy ageing. CMR imaging was performed in 236 individuals before training for a first-time marathon and 138 individuals were followed-up after marathon completion. After training, systolic/diastolic blood pressure reduced by 4/3mmHg, descending aortic stiffness decreased by 16%, and ventriculo-arterial coupling improved by 14%. LV mass increased slightly, with a tendency to more symmetrical hypertrophy. The reduction in aortic stiffness was equivalent to a 4-year reduction in estimated biological aortic age, and the benefit was greater in older, male, and slower individuals. In conclusion, this thesis demonstrates that automating analysis of clinical cardiovascular phenotypes is precise with significant time-saving. Complex data that is usually discarded can be used efficiently to identify new biology. Deeper phenotypes developed in this work inform risk reduction behaviour in healthy individuals, and demonstrably deliver a more sensitive marker of LV remodelling, potentially enhancing risk prediction in severe aortic stenosis