[BIC-announce] FW: Defence advertisement - Simon Duchesne

[Jennifer Chew, Ms.]

 FOR YOUR INFORMATION - Jennifer 



SIMON DUCHESNE

Ph.D. ORAL DEFENSE

DEPARTMENT OF BIOMEDICAL ENGINEERING

Date:		Tuesday, December 20, 2005 

Time:		10 a.m. 
  
Location:	De Grandpré Communications Centre
		Brain Tumor Research Centre
3801 University Street

Title:	Computer aided diagnosis in temporal lobe epilepsy and 
	Alzheimer's dementia


Abstract:
       
Computer aided diagnosis within neuroimaging must rely on advanced image processing techniques to detect and quantify subtle signal changes that may be surrogate indicators of disease state. This thesis proposes two such novel methodologies that are both based on large volumes of interest, are inherently data driven, and make use of cross-sectional scans: appearance-based classification (ABC) and voxel-based classification (VBC).

The concept of appearance in ABC represents the union of intensity and shape information extracted from magnetic resonance images (MRI). The classification method relies on a linear modeling of appearance features via principal components analysis, and comparison of the distribution of projection coordinates for the populations under study within a reference multidimentional appearance eigenspace. Classification is achieved using forward, stepwise linear discriminant analyses, in multiple cross-validated trials. In this work, the ABC methodology is shown to accurately lateralize the seizure focus in temporal lobe epilepsy (TLE), differentiate normal aging individuals from patients with either Alzheimer's dementia (AD) or Mild Cognitive Impairment (MCI), and finally predict the progression of MCI patients to AD. These applications demonstrated that the ABC technique is robust to different signal changes due to two distinct pathologies, to low resolution data and motion artifacts, and to possible differences inherent to multi-site acquisition.

The VBC technique relies on voxel-based morphometry to identify regions of grey and white matter concentration differences between co-registered cohorts of individuals, and then on linear modeling of variables extracted from these regions. Classification is achieved using linear discriminant analyses within a multivariate space composed of VBM-based measures related to grey and white matter concentration, along with clinical variables of interest. VBC is shown to increase the accuracy of prediction of one-year clinical status from three to four out of five TLE patients having undergone selective amygdalo-hippocampectomy.

These two techniques are shown to have the necessary potential to solve current problems in neurological research, assist clinical physicians with their decision-making process and influence positively patient management.

Jennifer Chew
McConnell Brain Imaging Centre
Montreal Neurological Institute
Room WB317
3801 University Street
Montreal, Quebec, Canada
H3A 2B4
Tel:  (514) 398-8554
Fax:  (514) 398-2975


-----Original Message-----
From: Pina Sorrini, Ms. 
Sent: Monday, December 12, 2005 5:16 PM
To: Simon Duchesne; Jennifer Chew, Ms.
Subject: RE: Defence advertisement

Absolutely, 

Jennifer here it is:


Thanks!

Pina

P.S.: I've already brought a copy at the MNI Director's office (6th floor) for posting.

-----Original Message-----
From: Simon Duchesne [mailto:duchesne at ieee.org]
Sent: Monday, December 12, 2005 5:12 PM
To: Jennifer Chew, Ms.; Pina Sorrini, Ms.
Subject: Defence advertisement

Pina / Jennifer

Thanks for advertising my defence on the BMED listings however, even though I don't really enjoy doing this, I think it is fair that the BIC at large learn of my defence (I've got a few collaborators within the different labs). So Pina, do you think you could send the invitation to Jennifer, and Jennifer, do you think it appropriate to advertise to the BIC ?

Thanks
Simon