Difference between revisions of "2012 Winter Project Week:PatchBased"
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| + | Project Title: '''A patch-based approach to the segmentation of organs at risk''' | ||
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<h3>Objective</h3> | <h3>Objective</h3> | ||
We will investigate the applicability of a patch-based approach in the context of label fusion. | We will investigate the applicability of a patch-based approach in the context of label fusion. | ||
| − | This could | + | This is interesting in scenarios, where the subjects show a high variability and consequently making the calculation of the deformation fields between them challenging. |
| + | One application of this method could be the automatic segmentation of organs at risk in the head and neck data. | ||
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<div style="width: 27%; float: left; padding-right: 3%;"> | <div style="width: 27%; float: left; padding-right: 3%;"> | ||
<h3>Approach, Plan</h3> | <h3>Approach, Plan</h3> | ||
| − | + | In order to evaluate the discriminative power of patches, we perform manifold learning to project points from high-dimensional patch space to lower dimensions. Having the low-dimensional embedding, we can assess if patches that correspond to the same label arrange in clusters. | |
| + | One challenge is the very large number of patches in an image or a group of images, posing problems to standard spectral methods for dimensionality reduction. Consequently we investigate the application of approaches to deal with a large number of samples, such as the Nyström method. | ||
</div> | </div> | ||
Revision as of 09:21, 5 January 2012
Home < 2012 Winter Project Week:PatchBasedProject Title: A patch-based approach to the segmentation of organs at risk
Investigators
- Christian Wachinger
- Polina Golland
Objective
We will investigate the applicability of a patch-based approach in the context of label fusion. This is interesting in scenarios, where the subjects show a high variability and consequently making the calculation of the deformation fields between them challenging. One application of this method could be the automatic segmentation of organs at risk in the head and neck data.
Approach, Plan
In order to evaluate the discriminative power of patches, we perform manifold learning to project points from high-dimensional patch space to lower dimensions. Having the low-dimensional embedding, we can assess if patches that correspond to the same label arrange in clusters. One challenge is the very large number of patches in an image or a group of images, posing problems to standard spectral methods for dimensionality reduction. Consequently we investigate the application of approaches to deal with a large number of samples, such as the Nyström method.
