This is a six-month extension to the previous three-year SNSF proposal (Grant 205321-124797).
Atlas-based segmentation is a key area of research that has a significant impact on diverse medical imaging applications. It has been shown in many recent works that automated segmentations obtained by the fusion of results from multiple atlases are more accurate and reliable than the results obtained from a single atlas. In our recent works, we developed a Markov Random Field (MRF) based fusion framework that generalizes many of the existing fusion methods; we have also proposed new atlas-fusion strategies and evaluated various fusion methods in the context of segmentation of lymph nodes in the Head and Neck (H&N) CT images.
The objective of the current project is to extend our MRF-based framework from a regular 3-D grid to a more general graph structure; this allows expanding our framework to other important applications, in particular, to the parcellation human cerebral cortex. We will be performing a comprehensive evaluation of various fusion methods for the parcellation of cerebral cortex.
More precisely we want to propose an extension of our current MRF-based fusion framework to a more general graph structure. Such a nice theoretical framework has several potential clinical applications; in particular, we want to focus on the application of “parcellation of human cerebral cortex”. We will be performing a comprehensive evaluation of various fusion methods that also incorporate an additional edge-preserving MRF-based smoothness term. Further, we would like to use this framework for the connectivity analysis of the baby brains as well. Depending on the results from different fusion methods, this work could ultimately result in constructing a more accurate general template for the parcellation of the human cerebral cortex.