Computational methods for medical image analysis

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The goal of the present grant is to extend the use of computer vision and machine learning methods in medical image analysis for two specific applications: (1) the inference of cardiac muscle fibers from echocardiography imaging, and (2) the image-based characterization and quantification of cancerous tumor appearance. Regarding the first application, the cardiac muscle fibers form the substrate of the cardiac muscle, and as such they are of major importance in cardiology. The ability to infer the geometry of cardiac muscle fibers from echocardiography, the most widely used imaging modality in the clinic, will allow the creation of patient-specific cardiac models to facilitate non-invasive diagnosis and early treatment. With regards to the second application, there has been a dramatic increase in therapeutic options for patients with cancer over the last few years, with the advent of a number of novel targeted drug therapies. However, given the extremely diverse nature of cancer, only a small number of patients are likely to fully benefit from any one particular targeted treatment. It is therefore important to be able to predict, early in the course of treatment, which patients are most likely to benefit from a particular treatment. This can be achieved with non-invasive imaging, together with algorithms for image analysis that can predict response to treatment based on features of tumor appearance.