Difference between revisions of "DBP3:Utah:AutoEndoSeg"
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Revision as of 18:28, 29 November 2011
Home < DBP3:Utah:AutoEndoSegContents
Notes on the Endocardial Segmentation Algorithm
Algorithm is a multi-atlas segmentation.
- Register (cropped) heart region of patient DEMRI to all of the n atlas DEMRI images and record n minimum metric values (mutual information)
- Transform all n atlas segmentations to patient DEMRI space
- Segmentation is a weighted average of the transformed atlas segmentation, where weights are determined by the mutual information values from step 1
- Pick a threshold as your segmentation
Notes on Yi's Slicer Module
- The exposed parameter is the ratio of pixel samples for mutual information to the total number of pixels in the image.
- We could also expose the threshold parameter in the final step
- We can change or add atlas datasets by copying into the appropriate directory and editing the text file that lists them
- Uses the ITK mutual information code. Does affine registration followed by b-spline
- We will partner with Yi for module development at the Jan 2012 project week
Initial Experiences
- Comparison of GA Tech and Utah-derived segmentations
(click to enlarge)
GA Tech Segmentation | Utah Segmentation | Thresholding Difference |
---|---|---|
Segmentation of an atlas image by GA Tech, using their endocardial auto-segmentation algorithm. | Segmentation of the same image with the same algorithm by Utah. | User-based variability in thresholding the probability mask output. (Green region - Utah; white region - GA Tech). |
Larger Atlas Images
- A second atlas was generated from cropped images that included a larger region of the initial MRI scans (namely, the appendage and a greater extent of the PVs was included in this second atlas).
- This atlas showed no marked improvement over the initial atlas
- Segmentations with the second atlas tended to not capture the entire blood pool, whereas, the first atlas tended to overestimate the blood pool.
- Registration issues?
Mask and LGE Image | Mask and Expert-defined Segmentation | |
---|---|---|
Example 1 | ||
Example 2 | ||
Example 3 | ||
Example 4 | ||
Atlas Image | ||
Cropped MRI image overlaid with the algorithm-defined blood pool. | Expert manual segmentations (white) overlaid with the algorithm-defined blood pool (green). |
Note: The last segmentation (red) was derived from an image in the atlas; the other segmentations (green) were derived from similarly-cropped images, not in the atlas.