Modeling AnatomicalVariability in Populations        Polina Golland             MIT
Biomedical Image Analysis        (MRI or CT)• Compared to Computer Vision:   – More constrained problem, defined metrics o...
Common solutions• Geometry and Statistics   – Geometric transformations      » real deformation and capture variability   ...
Anatomical Shape Analysis• Image-based descriptors of shape• Model of differences between two populations   – Discriminati...
Anatomical Heterogeneity• Population as a collection of homogeneous sub-groups   – Correlate clinical and demographic info...
Nonparametric Atlases for   Segmentation: Left Atrium• Left atrium segmentation:• Spatial prior for ablation scars:       ...
Functional Organization of the Brain• Cluster functional response profiles        Body   – Hierarchical model for populati...
Lessons Learned• Not a computer vision problem, but an interesting problem  that involves images   – Need to understand th...
Upcoming SlideShare
Loading in …5
×

Fcv appli science_golland

147 views

Published on

Published in: Technology, Business
0 Comments
0 Likes
Statistics
Notes
  • Be the first to comment

  • Be the first to like this

No Downloads
Views
Total views
147
On SlideShare
0
From Embeds
0
Number of Embeds
2
Actions
Shares
0
Downloads
1
Comments
0
Likes
0
Embeds 0
No embeds

No notes for slide

Fcv appli science_golland

  1. 1. Modeling AnatomicalVariability in Populations Polina Golland MIT
  2. 2. Biomedical Image Analysis (MRI or CT)• Compared to Computer Vision: – More constrained problem, defined metrics of success – Understanding and interpretation » Population studies vs. patient-specific predictions• Examples: – Image segmentation » Well defined organ/tissue, goal: match or exceed expert – Image registration/matching » Metric: how well it supports localization – fMRI analysis » How well the model predicts behavior
  3. 3. Common solutions• Geometry and Statistics – Geometric transformations » real deformation and capture variability – Probabilistic generative models » variability of images (MRI, CT) and signals (fMRI)• Examples: – Active shape/appearance models (Cootes and Taylor) – Mixture models with spatial priors (Wells, Van Leemput) – Mutual Information registration (Viola and Wells) – Diffeomorphic transformations (Miller, Thirion, Pennec) – Hierarchical models (Friston, Penny, Worsley, Genovese, Wells)• Outstanding challenges – Patient-specific predictions – Anatomical variability
  4. 4. Anatomical Shape Analysis• Image-based descriptors of shape• Model of differences between two populations – Discriminative modeling• Explicit representation of differences – Perturbation analysis Hippocampal shape in schizophrenia z   x NIPS’01, MedIA’05
  5. 5. Anatomical Heterogeneity• Population as a collection of homogeneous sub-groups – Correlate clinical and demographic information with the partition• Mixture model – Clustering of subjects into sub-populations – Average template for each sub-population – Simultaneously estimate the partition and the templates » Generalized EM algorithm with image registration Anatomical templates in aging Young Middle Old IEEE TMI ’09,’10
  6. 6. Nonparametric Atlases for Segmentation: Left Atrium• Left atrium segmentation:• Spatial prior for ablation scars: MICCAI Workshop on Statistical Atlases and Computational Models of the Heart ‘10
  7. 7. Functional Organization of the Brain• Cluster functional response profiles Body – Hierarchical model for population Face – Enables discovery of » Novel functional systems ? » Novel stimulus categories – Removes the need for spatial alignment Scene – Enables novel fMRI experiments Supported by NSF CRCNS NIPS’10, NeuroImage’10
  8. 8. Lessons Learned• Not a computer vision problem, but an interesting problem that involves images – Need to understand the problem and the goals – Work closely with the scientist• Well defined goals and (at times painful) ways to validate – Might change overtime• Closely related to computer vision, machine learning and probabilistic inference

×