Discovering High-Dimensional Biomarkers Using Statistical Shape Analysis

Shape analysis is of high interest to the biomedical sciences due to its potential to precisely locate morphological changes in structures due to growth, pathology, or treatment. Shape represents critical morphometric features not encoded in simple derived measurements such as volume. However, learning how to perform shape analysis properly can be overwhelming without the appropriate tools and guidance.

With this webinar, we will look into exploring modeling for objects of non-spherical topology, skeletal models, 4D models, and study-specific optimal model correspondence as well as advanced shape statistics methodologies. We will aim to equip webinar viewers with the ability to then run these methodologies independently through the SlicerSALT open source toolkit.

1. Introduce attendees to morphometric analysis and cover a few basic geometric concepts as compared with traditional metrics (e.g. volume).
   
2. Introduce SlicerSALT methodologies, including algorithms for modeling, analyzing, and visualizing high-dimensional data.
3. Demonstrate the capabilities of SlicerSALT using several widely available datasets.