[BIC-announce] FW: [Fwd: CIM-REPARTI Invited talk: Segmentation of Image Ensembles via Latent Atlases: May 20/10]

[Jennifer Chew, Ms.]

Hello everyone,

Please note the following, on behalf of Prof. Tal Arbel:

------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
Speaker: Tammy Riklin Raviv, Postdoctoral fellow, MIT
Date: Thursday, May 20, 2010
Time: 11am-12pm
Location: Zames Seminar room, McConnell Engineering room 437

Segmentation of Image Ensembles via Latent Atlases
---------------------------------------------------
The images acquired via medical imaging modalities are frequently subject to low signal-to-noise ratio, bias field and partial volume effects. These artifacts, together with the naturally low contrast between image intensities of some neighboring structures, make the extraction of regions of interest (ROIs) in clinical images a challenging problem. Probabilistic atlases, typically generated from comprehensive sets of manually labeled examples, facilitate the analysis by providing statistical priors for tissue classification and structure segmentation. However, the limited availability of training examples that are compatible with the images to be segmented renders the atlas-based approaches impractical in many cases.

In the talk I will present a generative model for joint segmentation of corresponding regions of interest in a collection of aligned images that does not require labeled training data. Instead, the evolving segmentation of the entire image set supports each of the individual segmentations. This is made possible by iteratively inferring a subset of the model parameters, called the spatial parameters, as part of the joint segmentation processes. These spatial parameters are defined in the image domain and can be viewed as a latent atlas. Our latent atlas formulation is based on probabilistic principles, but we solve it using partial differential equations and energy minimization criteria. We evaluate the method successfully for the segmentation of cortical and subcortical structures within different populations and of brain tumors in a single-subject multi-modal longitudinal experiment.


Bio
----
Tammy Riklin Raviv is a post-doctorate researcher at the Medical Vision group of CSAIL, MIT since January 2008. She is also affiliated with the Surgical Planning Laboratory of the Brigham & Women's Hospital and Harvard Medical School.

She received her PhD degree from the school of Electrical Engineering of Tel-Aviv University in 2008.

She received the B.Sc. degree in Physics (1993), and the M.Sc. degree in Computer Science (1999) from the Hebrew University of Jerusalem. Her areas of interests include Computer Vision and Medical Image Analysis.





Jennifer Chew
McConnell Brain Imaging Centre
MNI - WB317
3801 University Street
Montreal, Qc  H3A 2B4
Telephone:  514-398-8554
Fax:  514-398-2975