Fast catheter segmentation from echocardiographic sequences based on segmentation from corresponding X-ray fluoroscopy for cardiac catheterization interventions

© 2014 IEEE. Echocardiography is a potential alternative to X-ray fluoroscopy in cardiac catheterization given its richness in soft tissue information and its lack of ionizing radiation. However, its small field of view and acoustic artifacts make direct automatic segmentation of the catheters very challenging. In this study, a fast catheter segmentation framework for echocardiographic imaging guided by the segmentation of corresponding X-ray fluoroscopic imaging is proposed. The complete framework consists of: 1) catheter initialization in the first X-ray frame; 2) catheter tracking in the rest of the X-ray sequence; 3) fast registration of corresponding X-ray and ultrasound frames; and 4) catheter segmentation in ultrasound images guided by the results of both X-ray tracking and fast registration. The main contributions include: 1) a Kalman filter-based growing strategy with more clinical data evalution; 2) a SURF detector applied in a constrained search space for catheter segmentation in ultrasound images; 3) a two layer hierarchical graph model to integrate and smooth catheter fragments into a complete catheter; and 4) the integration of these components into a system for clinical applications. This framework is evaluated on five sequences of porcine data and four sequences of patient data comprising more than 3000 X-ray frames and more than 1000 ultrasound frames. The results show that our algorithm is able to track the catheter in ultrasound images at 1.3 s per frame, with an error of less than 2 mm. However, although this may satisfy the accuracy for visualization purposes and is also fast, the algorithm still needs to be further accelerated for real-time clinical applications

Automated assessment of echocardiographic image quality using deep convolutional neural networks

Myocardial ischemia tops the list of causes of death around the globe, but its diagnosis and early detection thrives on clinical echocardiography. Although echocardiography presents a huge advantage of a non-intrusive, low-cost point of care diagnosis, its image quality is inherently subjective with strong dependence on operators’ experience level and acquisition skill. In some countries, echo specialists are mandated to supplementary years of training to achieve ‘gold standard’ free-hand acquisition skill without which exacerbates the reliability of echocardiogram and increases possibility for misdiagnosis. These drawbacks pose significant challenges to adopting echocardiography as authoritative modalities for cardiac diagnosis. However, the prevailing and currently adopted solution is to manually carry out quality evaluation where an echocardiography specialist visually inspects several acquired images to make clinical decisions of its perceived quality and prognosis. This is a lengthening process and laced with variability of opinion consequently affection diagnostic responses. The goal of the research is to provide a multi-discipline, state-of-the-art solution that allows objective quality assessment of echocardiogram and to guarantee the reliability of clinical quantification processes. Computer graphic processing unit simulations, medical imaging analysis and deep convolutional neural network models were employed to achieve this goal. From a finite pool of echocardiographic patient datasets, 1650 random samples of echocardiogram cine-loops from different patients with age ranges from 17 and 85 years, who had undergone echocardiography between 2010 and 2020 were evaluated. We defined a set of pathological and anatomical criteria of image quality by which apical-four and parasternal long axis frames can be evaluated with feasibility for real-time optimization. The selected samples were annotated for multivariate model developments and validation of predicted quality score per frame. The outcome presents a robust artificial intelligence algorithm that indicate frames’ quality rating, real-time visualisation of element of quality and updates quality optimization in real-time. A prediction errors of 0.052, 0.062, 0.069, 0.056 for visibility, clarity, depth-gain, and foreshortening attributes were achieved, respectively. The model achieved a combined error rate of 3.6% with average prediction speed of 4.24 ms per frame. The novel method established a superior approach to two-dimensional image quality estimation, assessment, and clinical adequacy on acquisition of echocardiogram prior to quantification and diagnosis of myocardial infarction

A computer vision pipeline for fully automated echocardiogram interpretation

Cardiovascular disease is the leading cause of global mortality and continues to place a significant burden, in economic and resource terms, upon health services. A 2-dimensional transthoracic echocardiogram captures high spatial and temporal images and videos of the heart and is the modality of choice for the rapid assessment of heart function and structure due to it’s non-invasive nature and lack of ionising radiation. The challenging process of analysing echocardiographic images is currently manually performed by trained experts, though this process is vulnerable to intra- and inter-observer variability and is highly time-consuming. Additionally, echocardiographic images suffer from varying degrees of noise and vary drastically in terms of image quality. Exponential advancements in the fields of artificial intelligence, deep learning and computer vision have enabled the rapid development of automated systems capable of high-precision tasks, often out-performing human experts. This thesis aims to investigate the applicability of applying deep learning methods to automate key processes in the modern echocardiographic laboratory. Namely, view classification, quality assessment, cardiac phase detection, segmentation of the left ventricle and keypoint detection on tissue Doppler imaging strips. State-of-the-art deep learning architectures were applied to each task, and evaluated against ground-truth annotations provided by trained experts. The datasets used throughout each Chapter are diverse and, in some cases, have been made public for the benefit of the research community. To encourage transparency and openness, all code and model weights have been published. Should automated deep learning systems, both online (in terms of providing real-time feedback) and offline (behind the scenes), become integrated within clinical practice, there is great potential for improved accuracy and efficiency, thus improving patient outcomes. Furthermore, health services could save valuable resources such as time and money

Fast catheter segmentation and tracking based on x-ray fluoroscopic and echocardiographic modalities for catheter-based cardiac minimally invasive interventions

X-ray fluoroscopy and echocardiography imaging (ultrasound, US) are two imaging modalities that are widely used in cardiac catheterization. For these modalities, a fast, accurate and stable algorithm for the detection and tracking of catheters is required to allow clinicians to observe the catheter location in real-time. Currently X-ray fluoroscopy is routinely used as the standard modality in catheter ablation interventions. However, it lacks the ability to visualize soft tissue and uses harmful radiation. US does not have these limitations but often contains acoustic artifacts and has a small field of view. These make the detection and tracking of the catheter in US very challenging. The first contribution in this thesis is a framework which combines Kalman filter and discrete optimization for multiple catheter segmentation and tracking in X-ray images. Kalman filter is used to identify the whole catheter from a single point detected on the catheter in the first frame of a sequence of x-ray images. An energy-based formulation is developed that can be used to track the catheters in the following frames. We also propose a discrete optimization for minimizing the energy function in each frame of the X-ray image sequence. Our approach is robust to tangential motion of the catheter and combines the tubular and salient feature measurements into a single robust and efficient framework. The second contribution is an algorithm for catheter extraction in 3D ultrasound images based on (a) the registration between the X-ray and ultrasound images and (b) the segmentation of the catheter in X-ray images. The search space for the catheter extraction in the ultrasound images is constrained to lie on or close to a curved surface in the ultrasound volume. The curved surface corresponds to the back-projection of the extracted catheter from the X-ray image to the ultrasound volume. Blob-like features are detected in the US images and organized in a graphical model. The extracted catheter is modelled as the optimal path in this graphical model. Both contributions allow the use of ultrasound imaging for the improved visualization of soft tissue. However, X-ray imaging is still required for each ultrasound frame and the amount of X-ray exposure has not been reduced. The final contribution in this thesis is a system that can track the catheter in ultrasound volumes automatically without the need for X-ray imaging during the tracking. Instead X-ray imaging is only required for the system initialization and for recovery from tracking failures. This allows a significant reduction in the amount of X-ray exposure for patient and clinicians.Open Acces

Doctor of Philosophy

dissertationCongenital heart defects are classes of birth defects that affect the structure and function of the heart. These defects are attributed to the abnormal or incomplete development of a fetal heart during the first few weeks following conception. The overall detection rate of congenital heart defects during routine prenatal examination is low. This is attributed to the insufficient number of trained personnel in many local health centers where many cases of congenital heart defects go undetected. This dissertation presents a system to identify congenital heart defects to improve pregnancy outcomes and increase their detection rates. The system was developed and its performance assessed in identifying the presence of ventricular defects (congenital heart defects that affect the size of the ventricles) using four-dimensional fetal chocardiographic images. The designed system consists of three components: 1) a fetal heart location estimation component, 2) a fetal heart chamber segmentation component, and 3) a detection component that detects congenital heart defects from the segmented chambers. The location estimation component is used to isolate a fetal heart in any four-dimensional fetal echocardiographic image. It uses a hybrid region of interest extraction method that is robust to speckle noise degradation inherent in all ultrasound images. The location estimation method's performance was analyzed on 130 four-dimensional fetal echocardiographic images by comparison with manually identified fetal heart region of interest. The location estimation method showed good agreement with the manually identified standard using four quantitative indexes: Jaccard index, Sørenson-Dice index, Sensitivity index and Specificity index. The average values of these indexes were measured at 80.70%, 89.19%, 91.04%, and 99.17%, respectively. The fetal heart chamber segmentation component uses velocity vector field estimates computed on frames contained in a four-dimensional image to identify the fetal heart chambers. The velocity vector fields are computed using a histogram-based optical flow technique which is formulated on local image characteristics to reduces the effect of speckle noise and nonuniform echogenicity on the velocity vector field estimates. Features based on the velocity vector field estimates, voxel brightness/intensity values, and voxel Cartesian coordinate positions were extracted and used with kernel k-means algorithm to identify the individual chambers. The segmentation method's performance was evaluated on 130 images from 31 patients by comparing the segmentation results with manually identified fetal heart chambers. Evaluation was based on the Sørenson-Dice index, the absolute volume difference and the Hausdorff distance, with each resulting in per patient average values of 69.92%, 22.08%, and 2.82 mm, respectively. The detection component uses the volumes of the identified fetal heart chambers to flag the possible occurrence of hypoplastic left heart syndrome, a type of congenital heart defect. An empirical volume threshold defined on the relative ratio of adjacent fetal heart chamber volumes obtained manually is used in the detection process. The performance of the detection procedure was assessed by comparison with a set of images with confirmed diagnosis of hypoplastic left heart syndrome and a control group of normal fetal hearts. Of the 130 images considered 18 of 20 (90%) fetal hearts were correctly detected as having hypoplastic left heart syndrome and 84 of 110 (76.36%) fetal hearts were correctly detected as normal in the control group. The results show that the detection system performs better than the overall detection rate for congenital heart defect which is reported to be between 30% and 60%

Automated Echocardiographic Image Interpretation Using Artificial Intelligence

In addition to remaining as one of the leading causes of global mortality, cardio vascular disease has a significant impact on overall health, well-being, and life expectancy. Therefore, early detection of anomalies in cardiac function has become essential for early treatment, and therefore reduction in mortalities. Echocardiography is the most commonly used modality for evaluating the structure and function of the heart. Analysis of echocardiographic images has an important role in the clinical practice in assessing the cardiac morphology and function and thereby reaching a diagnosis. The process of interpretation of echocardiographic images is considered challenging for several reasons. The manual annotation is still a daily work in the clinical routine due to the lack of reliable automatic interpretation methods. This can lead to time-consuming tasks that are prone to intra- and inter-observer variability. Echocardiographic images inherently suffer from a high level of noise and poor qualities. Therefore, although several studies have attempted automating the process, this re-mains a challenging task, and improving the accuracy of automatic echocardiography interpretation is an ongoing field. Advances in Artificial Intelligence and Deep Learning can help to construct an auto-mated, scalable pipeline for echocardiographic image interpretation steps, includingview classification, phase-detection, image segmentation with a focus on border detection, quantification of structure, and measurement of the clinical markers. This thesis aims to develop optimised automated methods for the three individual steps forming part of an echocardiographic exam, namely view classification, left ventricle segmentation, quantification, and measurement of left ventricle structure. Various Neural Architecture Search methods were employed to design efficient neural network architectures for the above tasks. Finally, an optimisation-based speckle tracking echocardiography algorithm was proposed to estimate the myocardial tissue velocities and cardiac deformation. The algorithm was adopted to measure cardiac strain which is used for detecting myocardial ischaemia. All proposed techniques were compared with the existing state-of-the-art methods. To this end, publicly available patients datasets, as well as two private datasets provided by the clinical partners to this project, were used for developments and comprehensive performance evaluations of the proposed techniques. Results demonstrated the feasibility of using automated tools for reliable echocardiographic image interpretations, which can be used as assistive tools to clinicians in obtaining clinical measurements

Foetal echocardiographic segmentation

Congenital heart disease affects just under one percentage of all live births [1]. Those defects that manifest themselves as changes to the cardiac chamber volumes are the motivation for the research presented in this thesis. Blood volume measurements in vivo require delineation of the cardiac chambers and manual tracing of foetal cardiac chambers is very time consuming and operator dependent. This thesis presents a multi region based level set snake deformable model applied in both 2D and 3D which can automatically adapt to some extent towards ultrasound noise such as attenuation, speckle and partial occlusion artefacts. The algorithm presented is named Mumford Shah Sarti Collision Detection (MSSCD). The level set methods presented in this thesis have an optional shape prior term for constraining the segmentation by a template registered to the image in the presence of shadowing and heavy noise. When applied to real data in the absence of the template the MSSCD algorithm is initialised from seed primitives placed at the centre of each cardiac chamber. The voxel statistics inside the chamber is determined before evolution. The MSSCD stops at open boundaries between two chambers as the two approaching level set fronts meet. This has significance when determining volumes for all cardiac compartments since cardiac indices assume that each chamber is treated in isolation. Comparison of the segmentation results from the implemented snakes including a previous level set method in the foetal cardiac literature show that in both 2D and 3D on both real and synthetic data, the MSSCD formulation is better suited to these types of data. All the algorithms tested in this thesis are within 2mm error to manually traced segmentation of the foetal cardiac datasets. This corresponds to less than 10% of the length of a foetal heart. In addition to comparison with manual tracings all the amorphous deformable model segmentations in this thesis are validated using a physical phantom. The volume estimation of the phantom by the MSSCD segmentation is to within 13% of the physically determined volume

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

Echocardiographic/ Doppler criteria of normality, the findings in cardiac disease and the genetics of familial dilated cardiomyopathy in Newfoundland dogs

Dilated cardiomyopathy (DCM) is common in pedigree dog breeds including Newfoundlands. The breed predisposition and the familial prevalence within breeds support a genetic basis to the disease. Familial occurrence of DCM has only recently been recognised in man, and echocardiography abnormalities are common in relatives of DCM patients.Echocardiography is the method of choice for confirming the diagnosis of DCM. Echocardiographic/Doppler data are presented from 223 scans from 165 individual Newfoundland dogs. The scans were categorised into six groups based on the clinical presentation, M-mode echocardiography results and the Doppler derived aortic velocity. The Normal group showed no abnormalities (n=86). The DCM (overt or occult) group had a rounded left ventricle and fractional shortening (FS) <22% (n=35). There were two depressed fractional shortening groups, without other abnormalities; one with FS less than 18% (dFS<18%) (n=29) and the other with FS 18-20% (dFS18-20%) (n=24). The left ventricular enlargement (LVE) group was defined as a LV diastolic dimension greater than 55mm (males) or >50 mm (females), without any M-mode evidence of systolic dysfunction (n=8). Dogs with an aortic velocity exceeding 1.7 m/s were defined as showing evidence of subaortic stenosis (SAS group) (n=40).Data from complete echocardiographic/Doppler analysis of the Normal group were assessed for dependence on the gender, age and size of dog (weight or body surface area (BSA)) and the heart rate (mean R-R interval) by linear regression analyses. LV volumes and M-mode measurements were positively correlated with size. Gender was not an important predictor of most echo measurements once data was normalised for BSA. Advancing age was a significant negative predictor of LV volumes and dimensions although influence on wall thickness was not significant. Age also showed a significant influence on diastolic function, assessed by mitral inflow and pulmonary venous flow, similar to changes described in man.The Newfoundland groups were compared. The DCM group had significantly increased LV volume and dimensions and decreased systolic function than other groups. There were few significant differences between the groups for diastolic function parameters. There was considerable overlap between groups for all dimensions and the parameters of systolic function, although the pre-ejection period: ejection time (PEP:ET) ratio appeared to be most sensitive for distinguishing normal from DCM dogs. In both dFS groups and the LVE group, this ratio was intermediate between the Normal and DCM groups, in contrast to other parameters of systolic function. Left atrial dysfunction was also identified in the DCM group, but was less marked in both dFS and the LVE groups. Some dogs in the LVE and dFS groups progressed to develop DCM but a longer duration of study would be required before firm conclusions can be drawn about progression.Most of the dogs in this study were related. Pedigree and segregation analyses were supportive but not conclusive for an autosomal dominant mode of inheritance for DCM. A simulated linkage analysis indicated that this family was sufficiently informative for a genetic linkage analysis study. A pilot study assessing a number of anonymous canine microsatellites confirmed that there was sufficient heterozygosity and polymorphism, despite significant inbreeding, to permit a genetic linkage analysis study. No significant LOD score was achieved for the twelve microsatellites assessed. However, the available data indicate that a genome-wide linkage analysis is likely to be successful, with phenotyping based on the echocardiography data