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A. Criminisi, J. Shotton, S. Bucciarelli and K. Siddiqui

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Presentation on theme: "A. Criminisi, J. Shotton, S. Bucciarelli and K. Siddiqui"— Presentation transcript:

1 A. Criminisi, J. Shotton, S. Bucciarelli and K. Siddiqui

2 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

3 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

4 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

5 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

6 No contrast agent … Considerable geometric variations. Conventional atlas-based techniques would not work.

7 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.

8 Node optimization function Training a single tree class S S1S1 S2S2 During training each node sees only a random subset of all available features Each tree is training independently, using the same procedure

9 Posterior output of classifier Organ detection Organ localization Testing Using multiple trees has been shown to improve generalization.

10 Context-rich visual features, a 2D illustration 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 by the displaced integration regions.

11 Results of automatic organ detection and localization for three different patients.

12 Our algorithm Our algorithm Gaussian Mix. Model Gaussian Mix. Model Template matching Template matching (multiple runs on multiple train/test splits)

13 More anatomical structures Hierarchical -> Finer structures Spatial priors for greater robustness to noise Larger training database


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