DBP3:Utah:AutoEndoSeg

Notes on the Endocardial Segmentation Algorithm

Algorithm is a multi-atlas segmentation.

1. Register (cropped) heart region of patient DEMRI to all of the n atlas DEMRI images and record n minimum metric values (mutual information)
2. Transform all n atlas segmentations to patient DEMRI space
3. Segmentation is a weighted average of the transformed atlas segmentation, where weights are determined by the mutual information values from step 1
4. 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
	Cropped MRI image overlaid with the algorithm-defined blood pool.	Expert manual segmentations (white) overlaid with the algorithm-defined blood pool (green).