Segmenting cortical and sub-cortical structures from 3D brain images is of significant practical importance. In this talk, we will discuss a new statistical modeling/computing framework and show its application for whole brain segmentation.
The notion of using context information for solving the medical imaging problem has been increasingly realized in the field. However, how to learn an effective and efficient context model, together with the image appearance, remains mostly unknown. The current literature using Markov Random Fields (MRFs) and Conditional Random Fields (CRFs) often involves specific algorithm design, in which the modeling and computing stages are studied in isolation. Medical images observe complex patterns, contributed by many factors such as textures (homogeneous, inhomogeneous, and structured) and machine parameters. This auto-context model is about a new attempt to push the appearance and context information in a seamless way by automatically incorporating a large number of short-range and long-range features. The resulting algorithm has nearly the identical procedures in computing (testing) as in modeling (training), and thus, achieves rapid performance the holistic medical image segmentation task. We will show a variety of sub-cortical and cortical segmentation results using this model.
Audio (MP3 File, Podcast Ready)
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