Date post: | 27-Mar-2015 |
Category: |
Documents |
Upload: | landon-bolton |
View: | 222 times |
Download: | 6 times |
A. Criminisi, J. Shotton, S. Bucciarelli and K. Siddiqui
http://research.microsoft.com/en-us/projects/medicalimageanalysis/
One-click visual navigation
Better visualization (class-driven col. transfer functions)
Initialization for organ-specific processing
Content-driven image search
Applications
One-click visual navigation
Setting up the ideal 3D view for diagnosing problems with heart valves is laborious.
Applications One-click visual navigation
Better visualization (class-driven col. transfer functions)
Initialization for organ-specific processing
Content-driven image search
If we know where the liver is then we can start an automatic process for detecting calcifications.
Applications One-click visual navigation
Better visualization (class-driven col. transfer functions)
Initialization for organ-specific processing
Content-driven image search
If we know where the liver is then we can start an automatic process for detecting calcifications.
Applications One-click visual navigation
Better visualization (class-driven col. transfer functions)
Initialization for organ-specific processing
Content-driven image search
No contrast agent
…
Considerable geometric variations. Conventional atlas-based techniques would not work.
Labelling via axis aligned 3D bounding boxes.
Classes = heart, liver, l. kidney, r. kidney, l. lung, r. lung, l. eye, r. eye, head, background
Positive and negative training examples for organ centres.
Node optimization functionNode optimization function
Training a single tree
class
class
class
class
class
S
S1 S2
During training each node “sees” only a random subset of all available features
Each tree is training independently, using the same procedure
Posterior output of classifierPosterior output of classifier
Organ detectionOrgan detection
Organ localizationOrgan localization
Testing
Using multiple trees has been shown to improve generalization.
Context-rich visual features, a 2D illustration
Feature response Feature response
Lots and lots of randomly generated features. Out of those the most discriminative ones are selected automatically during training.
Long-range spatial context is captured bythe displaced integration regions.
Results of automatic organ detection and localization for three different patients.
• Our algorithmOur algorithm
• Gaussian Mix. ModelGaussian Mix. Model
• Template matchingTemplate matching
(multiple runs on multiple train/test (multiple runs on multiple train/test splits)splits)
More anatomical structures
Hierarchical -> Finer structures
Spatial priors for greater robustness to noise
Larger training database
http://research.microsoft.com/en-us/projects/medicalimageanalysis/