View source

== Announcements ==

The seminar takes place Monday 12:15-1:15 PM in the WEB 3760. See the SCI calendar and subscribe to the google calendar below.

Archived agendas are now available for previous presentations

For scheduling and help with subscribing to the mailing list contact [http://www.sci.utah.edu/people/bowang.html Bo Wang]

== Events Calendar ==
<googlecalendar>pue3310h1j7t6rv857utmj0a60@group.calendar.google.com&color=%23A32929&src=e1d2k21gofdkflh4dn7cr8lmd4@group.calendar.google.com</googlecalendar>

Click the 'Add Calendar' button above to add the imaging calendar to your personal google calendar.


== Weekly Agendas (Fall 2010) ==

===September 13rd===

<u>Presenter</u>: '''Linh Ha''' - Presentation of MICCAI 2010. 

<u>Presentation Title</u>: Image Registration Driven by Combined Probabilistic and Geometric Descriptors

<u>Description/Abstract</u>: 

Deformable image registration in the presence of considerable
contrast differences and large-scale size and shape changes represents
a significant challenge for image registration. A representative driving
application is the study of early brain development in neuroimaging,
which requires co-registration of images of the same subject across time
or building 4-D population atlases. Growth during the first few years of
development involves significant changes in size and shape of anatomical
structures but also rapid changes in tissue properties due to myelination
and structuring that are reflected in the multi-modal Magnetic
Resonance (MR) contrast measurements. We propose a new registration
method that generates a mapping between brain anatomies represented
as a multi-compartment model of tissue class posterior images and geometries.
We transform intensity patterns into combined probabilistic
and geometric descriptors that drive the matching in a diffeomorphic
framework, where distances between geometries are represented using
currents which does not require geometric correspondence. We show preliminary
results on the registrations of neonatal brain MRIs to two-year
old infant MRIs using class posteriors and surface boundaries of structures
undergoing major changes. Quantitative validation demonstrates
that our proposed method generates registrations that better preserve
the consistency of anatomical structures over time

<u>Supplemental material</u>: [http://www.sci.utah.edu/~prastawa/papers/MICCAI2010_Ha_ProbCurrents.pdf Paper]


<u>Presenter</u>: '''Fangxiang Jiao''' - Presentation of MICCAI-MIAR 2010. 

<u>Presentation Title</u>: Metrics for Uncertainty Analysis and Visualization of Diffusion Tensor Images

<u>Description/Abstract</u>: 

In this paper, we propose three metrics to quantify the
differences between the results of diffusion tensor magnetic
resonance imaging (DT-MRI) fiber tracking algorithms: the area
between corresponding fibers of each bundle, the Earth Mover's
Distance (EMD) between two fiber bundle volumes, and the current
distance between two fiber bundle volumes.  We also discuss an
interactive fiber track comparison visualization toolkit we have
developed based on the three proposed fiber difference metrics and
have tested on six widely-used fiber tracking algorithms.  To show
the effectiveness and robustness of our metrics and visualization
toolkit, we present results on both synthetic data and high
resolution monkey brain DT-MRI data.  Our toolkit can be used
for testing the noise effects on fiber tracking analysis
and visualization and to quantify the difference between any pair
of DT-MRI techniques,  compare single subjects within
an image atlas.

<u>Supplemental material</u>: [http://www.cs.utah.edu/~jeffp/papers/fiber-uncertainty.pdf Paper]


===September 20th===

===September 27th===

===October 4th===

<u>Presenter</u>: '''Stanley Durrleman'''

<u>Additional information</u>: Dr. Stanley Durrleman is a postdoctoral fellow at SCI. His research interests are statistical analysis of 3D shapes in the context of Computational Anatomy, correspondence-free metric between shapes like currents, and registration using large diffeomorphic deformation. Dr. Stanley Durrleman's SCI page is http://www.sci.utah.edu/people/stanley.html


===October 11st===

'''This week will be the Fall break'''

===October 18th===

===October 25th===

===November 1st===

===November 8th===

===November 15th===

===November 22nd===

===November 29th===

===December 6th===

===December 13rd===



== Past Weekly Agendas ==


===August 23rd===

<u>Presenter</u>: '''Xiang Hao, Bo Wang'''

<u>Presentation Title</u>: Review of Computer Vision and Pattern Recognition (CVPR) 2010

<u>Description/Abstract</u>: 

Xiang Hao and Bo Wang went to Computer Vision and Pattern Recognition (CVPR) 2010 (June 13 - 18), they will give a review of CVPR 2010. 

<u>Supplemental material</u>:

The Oral Presentations and Poster Spotlights of CVPR 2010 [http://videolectures.net/cvpr2010_san_francisco/ video]

List of Tutorials/Short Courses of CVPR 2010 [http://cvl.umiacs.umd.edu/conferences/cvpr2010/tutorials/ slides]


===August 30th===

<u>Presenter</u>: '''Stephanie Allassonniere'''

<u>Presentation Title</u>: A Stochastic Algorithm for Probabilistic Independent Component Analysis

<u>Description/Abstract</u>: 

The decomposition of a sample of images on a relevant subspace is a recurrent problem in many different fields from Computer Vision to medical image analysis. We propose in this paper a new learning principle and implementation of the generative decomposition model generally known as noisy ICA (for independent component analy- sis) based on the SAEM algorithm, which is a versatile stochastic approximation of the standard EM algorithm. We demonstrate the applicability of the method on a large range of decomposition models and illustrate the developments with experimental results on various data sets.

<u>Additional information</u>: Dr. Stephanie Allassonniere is a visiting professor at SCI, she is doing research in statistical analysis of images and deformations. Dr. Stephanie Allassonniere's SCI page is http://www.sci.utah.edu/people/sallassonniere.html


===September 6th===

Labor Day holiday


[[Archived Weekly Agendas for Fall 2009]]

[[Archived Weekly Agendas for Spring 2010]]

== Imaging Mailing List ==

HOW TO SUBSCRIBE OR UNSUBSCRIBE YOURSELF:

Send an email to sympa@lists.sci.utah.edu

Where the subject of the email is the following:
   subscribe imaging someone@foobar.com

You will get an email confirmation shortly after.

== Software Documentation ==

=== [[NCR_Toolset|Neural Circuit Reconstruction Toolset]] ===

== Software Development ==

=== [[Meetings]] ===

=== [[Tools In Development]] ===

=== [[Future Work]] ===