Vessel-based Framework for Joint Image Registration and Segmentation

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Deformable image registration and segmentation can provide the doctors with better image quality and vital information and therefore is becoming the essential part in medical treatment planning and diagnosis. In this proposal we will investigate new deformable image processing techniques including intelligent joint image registration and segmentation, vessel segmentation and deformable image registration of inhomogeneous soft tissues. The outcome of this research will provide an effective way to reduce doctors' reading time and improve accuracy and quality of diagnosis and treatment planning. The technologies developed will be used for the treatment planning and guidance in surgical procedures on the liver and can also be extended to the lungs, the brain and the heart. ***Most of physics based deformable registration research works assume that the tissues are homogeneous. However, this assumption is not valid in many medical applications. For example, in order to help oncologist/surgeons to determine the status of malicious tumors, we often need to align the regions with embedded tumors, homogeneity cannot be assumed in these cases because the tumor and the surrounding tissues have very different properties of stiffness and elasticity. Assumption of homogeneity would result in large discrepancy in registration accuracy, as we have observed in our research work on image registration of liver images. To address this problem, we propose to partition the whole region of interest into smaller sub-regions and dynamically adjust weights of vessel segments and bifurcation points in each sub-region in the registration objective function so that we are able to achieve accurate registration for the whole region including the neighborhood of the tumors. ***In feature based image registration techniques, accurate image registration to a large extent relies on accurate image segmentation. Blood vessels are critical structures of many organs and can provide fundamental information for image registration. They are also good references for localizing treatment targets deep inside organs. Accurate segmentation of vessels not only can provide useful information for registration, but also can be useful for detecting registration errors in regions with vessels. Accurate and fast segmentation is still a very challenging task due to the complexity and variability of deformable soft tissues/organs. There are some unique difficulties that are associated with vessel segmentation including missing vessels, disconnected vessels, touching vessels, noise, vague and incomplete boundaries, inadequate contrast of images. In this proposal, we will examine these challenges and investigate the problems using patient-specific guidance models produced from pre-acquired high quality images. **