Difference between revisions of "DBP3:Utah:AutoEndoSeg"

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= Larger Atlas Images =
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* 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).
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* This atlas showed no marked improvement over the initial atlas
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* Segmentations with the second atlas tended to not capture the entire blood pool, whereas, the first atlas tended to overestimate the blood pool.
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* Registration issues?
  
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= Larger Atlas Images =
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{| class="wikitable"
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|-
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! 
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! Mask and LGE Image
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! Mask and Expert-defined Segmentation
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|-
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| Example 1
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| [[File:CARMA_N24_LGE.png|thumb|center]]
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| [[File:CARMA_N24_Endo.png|thumb|center]]
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|-
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| Example 2
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| [[File:CARMA_N25_LGE.png|thumb|center]]
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| [[File:CARMA_N25_Endo.png|thumb|center]]
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|-
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| Example 3
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| [[File:CAMRA_N26_LGE.png|thumb|center]]
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| [[File:CARMA_N26_Endo.png|thumb|center]]
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|-
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| Example 4
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| [[File:CAMRA_N26_2_LGE.png|thumb|center]]
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| [[File:CARMA_N26_2_Endo.png|thumb|center]]
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|-
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| 
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| Cropped MRI image overlaid with the algorithm-defined blood pool.
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| Expert manual segmentations (white) overlaid with the algorithm-defined blood pool (green).
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|-
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Revision as of 08:49, 29 November 2011

Home < 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
CARMA GAT Endo.png
CARMA UT Endo.png
CARMA Diff Endo.png
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
CARMA N24 LGE.png
CARMA N24 Endo.png
Example 2
CARMA N25 LGE.png
CARMA N25 Endo.png
Example 3
CAMRA N26 LGE.png
CARMA N26 Endo.png
Example 4
CAMRA N26 2 LGE.png
CARMA N26 2 Endo.png
Cropped MRI image overlaid with the algorithm-defined blood pool. Expert manual segmentations (white) overlaid with the algorithm-defined blood pool (green).
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