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University of North Carolina Comparison of Human and M-rep Kidneys Segmented from CT Images James Chen, Gregg Tracton, Manjari Rao, Sarang Joshi, Steve Pizer, Joshua Stough, and Ed Chaney University of North Carolina Chapel Hill
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Methods [ Target Images: 12 planning CT images from archives (24 kidneys) [ Human (A and B) segmentation: Careful slice-by-slice pixel painting (voxel-based) [ Computer segmentation: Deformable m-reps (yields smooth surface) [ Comparison metrics reported here: Volume overlap and mean surface separation
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University of North Carolina The median volume overlap is measured by the overlap volume divided by the union of the two volumes being compared.
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Image Challenges Poor contrast (no contrast agent) Crowded soft tissue environment
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Image Challenges Motion artifacts
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Image Challenges Image variability near renal pelvis Three human segmentations of renal pelvis
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Three Stages of M-rep Segmentation Similarity + elongation Atom deformation Boundary displacement Implied surface after each scale of segmentation
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University of North Carolina Result after Final Stage
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Limitations of Comparison [ Segmented structures represented as collections of whole 2 mm 3 voxels [ Conversion of m-rep surfaces to voxels introduces a bias that favors humans [ Sensitivity of distance ~ 2 mm [ Best possible volume overlap is ~ 95%
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Results: Volume Overlap Human A to Human B: 90%-96% M-rep to Human A or B: 85%-95%
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University of North Carolina Case Human A vs B volume overlap Kidney Number
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University of North Carolina M-rep vs Human A volume overlap Case
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University of North Carolina
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Results: Average Surface Separation Human A to Human B: Within one voxel M-rep to Human A or B: Within one voxel * * with one exception
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University of North Carolina Image Image Human Human M-rep M-rep Outlier Case
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Who is right (Human or M-rep)? Movie Loop
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Conclusions In this study m-reps compared with humans A and B as well as A compared with B.
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University of North Carolina Sunset at Portsmouth Island
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