This project will develop a framework to represent, analyze and interpret shapes extracted from images, supporting a wide range of biological investigations. The primary objectives are: (1) to develop a mathematical framework and computational tools for the quantification and analysis of shapes; (2) to integrate these computational models with machine learning and statistical inference methods to enable new discoveries, transforming imaging data into biological knowledge; (3) to deliver novel quantitative methodologies for shape analysis that start from a biological premise, rather than a purely geometric one. The aim is thus not only to quantitatively describe shape, but to develop methods for linking morphological variation to its underlying biological causes. To ensure that the project focuses on methods that are most promising to biology with significant breadth of application, model and tool development will be guided and supported by a set of diverse case studies, ranging from the sub-cellular to organismal scales.
Shape represents a complex and rich source of biological information that is fundamentally linked to underlying mechanisms and function. However, shape is still often examined on a qualitative basis in many disciplines in biology, an approach that is time consuming and prone to human subjectivity. While ad hoc quantitative methods do exist, they are often inaccessible to non-experts and do not easily generalize to a wide variety of problems. The inability of biologists to systematically link shape to genetics, development, environment, function and evolution often precludes advances in biological research spanning diverse spatial and temporal scales, from the movement of molecules within a cell to adaptive changes in organismal morphology. The primary goal of this project is to develop a new suite of widely applicable quantitative methods and tools into the study of biological shape to address the significant need for consistent and repeatable analysis of shape data.
Blood flow conditions modulate cardiac development, such that abnormal blood flow during early stages of cardiac development can lead to cardiac defects even in the absence of genetic anomalies. This is important because it means that blood flow conditions (sometimes referred to as an "environmental factor") can affect embryonic development, and thus that genetic and hemodynamic factors are interlinked, that is, they affect each other. However, the mechanisms by which blood flow affect cardiac development are not well understood and prediction of the effects of altered blood flow on cardiac development are still elusive. The goal of this project was to develop computational algorithms and tools to facilitate the analysis of cardiac motion, cardiac morphology, and cardiac blood flow, to ultimately elucidate the effects of hemodynamic conditions on early embryonic cardiac development. Embryonic cardiac motion can be tracked using optical techniques. In particular, optical coherence tomography (OCT), from which Doppler blood flow velocities can also be obtained, has been extensively used to image and analyze the cardiac motion of early embryonic hearts in animal models. 4D images (images showing the 3D structure of the heart as it beats over time) can be obtained and have been routinely obtained by several research labs to better understand cardiac motion. However, analysis of images and image data sets currently constitutes a ‘bottle neck’ in the development of advanced techniques to understand the influence of hemodynamic conditions on cardiac motion. While manual analysis is possible and broadly used, the amount of images collected for a 4D image set, and the amount of information contained on those images make a ‘manual analysis’ impractical. This project has contributed computational algorithms and tools to more efficiently analyze cardiac shape and motion from OCT images, as well as computational models of the moving developing heart to better understand blood flow dynamics within the embryonic cardiac outflow tract. We developed and implemented algorithms to extract cardiac shape and motion from 4D OCT images of embryonic hearts, to subsequently visualize and quantify embryonic cardiac shape and motion, and to determine cardiac blood flow velocities. This is the first time that shape and motion are thoroughly analyzed in the context of cardiac development, and that tools are developed to facilitate such analysis. Using our developed tools we found, for example, that the characteristic peristaltic-like motion of the embryonic heart – at early developmental stages the heart has a tubular structure and resembles a peristaltic pump – is affected by altered blood flow conditions. The implications of this change are not trivial. This is because cardiac mechanics, not just blood flow, affect the way in which the heart develops. An abnormal cardiac motion, therefore, can trigger biological mechanisms that will affect the way in which the hearts develops. Further, alterations in blood flow conditions not only affect cardiac motion but also alter mechanical stimuli on cardiac cells, which in turn influence cardiac development. Sophisticated tools and algorithms are therefore required to understand the intricate relationship between blood flow, cardiac mechanics, and cardiac development. This project has provided the initial sophisticated tools required to elucidate how mechanics and blood flow affect embryonic cardiac development. The benefits of this project to society are manifold. On one end, developed tools will contribute to a basic understanding of the processes by which the heart develops from an initial tube to a four chambered heart. On another end, such basic knowledge will also provide insight into how cardiac malformations occur and the role of blood flow conditions in the development of cardiac defects. Analysis of cardiac motion will thus contribute to our understanding of how physical stresses and strains on cells affect cardiac development, and in general to the area of mechanobiology. This basic knowledge is important, because one day it could provide answers to parents of babies with congenital heart disease, and it could result on preventive strategies or ways of repairing the heart before the baby is born. Beyond cardiac formation, the algorithms and tools developed can also be broadly used in other areas of interest. As an example, we have been using tools developed in this project to analyze abdominal aortic aneurysms (AAAs). An AAA is an abnormal dilation of the abdominal aortic artery. If untreated, and AAA might rupture, with rupture carrying about 90% mortality rates. Once detected, therefore, AAAs are either repaired (if maximum diameter exceeds 5.5cm) or followed over time. Currently, follow up is done by imaging, but only maximum diameter is extracted from the images. It is not uncommon, however, that patients that are under surveillance present with rupture. Our developed tools are providing the opportunity to analyze and quantify shape changes in the AAA over time from follow-up images to determine if a more thorough analysis will be more precise in determining when to repair an AAA.
