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%

Methodological approach for the assessment of ultrasound reproducibility of cardiac structure and function: a proposal of the study group of Echocardiography of the Italian Society of Cardiology (Ultra Cardia SIC) Part I

When applying echo-Doppler imaging for either clinical or research purposes it is very important to select the most adequate modality/technology and choose the most reliable and reproducible measurements. Quality control is a mainstay to reduce variability among institutions and operators and must be obtained by using appropriate procedures for data acquisition, storage and interpretation of echo-Doppler data. This goal can be achieved by employing an echo core laboratory (ECL), with the responsibility for standardizing image acquisition processes (performed at the peripheral echo-labs) and analysis (by monitoring and optimizing the internal intra- and inter-reader variability of measurements). Accordingly, the Working Group of Echocardiography of the Italian Society of Cardiology decided to design standardized procedures for imaging acquisition in peripheral laboratories and reading procedures and to propose a methodological approach to assess the reproducibility of echo-Doppler parameters of cardiac structure and function by using both standard and advanced technologies. A number of cardiologists experienced in cardiac ultrasound was involved to set up an ECL available for future studies involving complex imaging or including echo-Doppler measures as primary or secondary efficacy or safety end-points. The present manuscript describes the methodology of the procedures (imaging acquisition and measurement reading) and provides the documentation of the work done so far to test the reproducibility of the different echo-Doppler modalities (standard and advanced). These procedures can be suggested for utilization also in non referall echocardiographic laboratories as an "inside" quality check, with the aim at optimizing clinical consistency of echo-Doppler data

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

3D echocardiographic reference ranges for normal left ventricular volumes and strain: Results fromthe EACVI NORRE study

Aim To obtain the normal ranges for 3D echocardiography (3DE) measurement of left ventricular (LV) volumes, function, and strain from a large group of healthy volunteers. Methods and results A total of 440 (mean age: 45613 years) out of the 734 healthy subjects enrolled at 22 collaborating institutions of the Normal Reference Ranges for Echocardiography (NORRE) study had good-quality 3DE data sets that have been analysed with a vendor-independent software package allowing homogeneous measurements regardless of the echocardiographic machine used to acquire the data sets. Upper limits of LV end-diastolic and end-systolic volumes were larger in men (97 and 42 mL/m2) than in women (82 and 35 mL/m2; P<0.0001). Conversely, lower limits of LV ejection fraction were higher in women than in men (51% vs. 50%; P<0.01). Similarly, all strain components were higher in women than in men. Lower range was -18.6% in men and -19.5% in women for 3D longitudinal strain, -27.0% and -27.6% for 3D circumferential strain, -33.2% and -34.4% for 3D tangential strain and 38.8% and 40.7% for 3D radial strain, respectively. LV volumes decreased with age in both genders (P<0.0001), whereas LV ejection fraction increased with age only in men. Among 3DE LV strain components, the only one, which did not change with age was longitudinal strain. Conclusion The NORRE study provides applicable 3D echocardiographic reference ranges for LV function assessment. Our data highlight the importance of age- and gender-specific reference values for both LV volumes and strain. All rights reserved

Computer vision and machine learning for medical image analysis: recent advances, challenges, and way forward.

The recent development in the areas of deep learning and deep convolutional neural networks has significantly progressed and advanced the field of computer vision (CV) and image analysis and understanding. Complex tasks such as classifying and segmenting medical images and localising and recognising objects of interest have become much less challenging. This progress has the potential of accelerating research and deployment of multitudes of medical applications that utilise CV. However, in reality, there are limited practical examples being physically deployed into front-line health facilities. In this paper, we examine the current state of the art in CV as applied to the medical domain. We discuss the main challenges in CV and intelligent data-driven medical applications and suggest future directions to accelerate research, development, and deployment of CV applications in health practices. First, we critically review existing literature in the CV domain that addresses complex vision tasks, including: medical image classification; shape and object recognition from images; and medical segmentation. Second, we present an in-depth discussion of the various challenges that are considered barriers to accelerating research, development, and deployment of intelligent CV methods in real-life medical applications and hospitals. Finally, we conclude by discussing future directions

Propaedeutics of internal medicine. Collection of clinical lectures: the educational and visual guide: in two parts. Part 1

A textbook designed for the training of specialists of the second (master's) level of higher education, educational qualification "Master of Medicine", professional qualification "Doctor" for English speaking students. Учбово-наочний посібник «Propaedeutics of internal medicine. Collection of clinical lectures» є виданням «Курсу лекцій з пропедевтики внутрішньої медицини» в авторській редакції, призначений для підготовки фацівців другого (магістерського) рівня вищої освіти, освітньої кваліфікації «Магістр медицини» професійної кваліфікації «Лікар», які навчаються на англійській мові