Title: 3D Medical Image Segmentation by Deformable M-Reps
Speaker: Stephen M. Pizer, Kenan Professor of Computer Science, Radiology, Radiation Oncology, & Biomedical Engineering, University of North Carolina, Chapel Hill.
Date: Monday, November 5, 2001
Time: 9:30-11:30 am
Place: 1245 Seamans Center (the electronic classroom)
3D segmentation of objects in 3D images can be usefully done by deforming structural models of anatomic objects into target images, the models having been built from training images. M-reps, sampled medial volume representations, are a means of representing the models that are particularly apt in this framework because they have special capabilities in deformability and intuitiveness, and provide good capabilities of many levels of scale and thus efficiency for any level of performance. After briefly describing m-reps models, this talk will focus on multiscale methods for deforming them into target images to segment objects of interest for radiotherapy treatment planning of the abdomen and male pelvis and for neuroscience of brain structures. These methods involve successive optimizations of objective functions summing a geometric typicality measure and a geometry to image match measure. Each of these measures will be described, as will the role, in each, of the spatial correspondence provided by m-reps. Results of segmenting a number of anatomic structures from CT and MR images will be illustrated. Quantitative results comparing inter-human kidney segmentation differences to mreps-to-human segmentation differences will be given.