Subj:   Seminar: Statistical Shape Analysis of Anatomical Structures

Thanks to Peter Yim (CC/DRD) for arranging and letting us know about
the following seminar:

Title:  Statistical Shape Analysis of Anatomical Structures
Speaker:  Dr. Polina Golland
          MIT AI Lab
Date:   10/1, Monday
Time:   11:00 am
Place:  LDRR conference room (B1N256)
Abstract: (see below)
Other Notes:  There are still a few slots available if anyone would
               like to meet with Dr. Golland.  If so, please contact
               Peter Yim (PYim@nih.gov)

We present a computational framework for image-based statistical
analysis of anatomical shape in different populations. Applications of
such analysis include understanding developmental and anatomical
aspects of disorders when comparing patients vs. normal controls,
studying morphological changes caused by aging, or even differences in
normal anatomy, for example, differences between genders.

Once a quantitative description of organ shape is extracted from input
images, the problem of identifying differences between the two groups
can be reduced to one of the classical questions in machine learning,
namely constructing a classifier function for assigning new examples
to one of the two groups while making as few mistakes as possible. In
the traditional classification setting, the resulting classifier is
rarely analyzed in terms of the properties of the input data that are
captured by the discriminative model. In contrast, interpretation of
the statistical model in the original image domain is an important
component of morphological analysis. We propose a novel approach to
such interpretation that allows medical researchers to argue about the
identified shape differences in anatomically meaningful terms of organ
development and deformation. For each example in the input space, we
derive a discriminative direction that corresponds to the differences
between the classes implicitly represented by the classifier
function. For morphological studies, the discriminative direction can
be conveniently represented by a deformation of the original shape,
yielding an intuitive description of shape differences for
visualization and further analysis.

Based on this approach, we present a system for statistical shape
analysis using distance transforms for shape representation and the
Support Vector Machines learning algorithm for the optimal classifier
estimation. We demonstrate it on artificially generated data sets, as
well as real medical studies.