EMSegmenter-Tasks:CT-Hand-Bone

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Description

Single channel automatic segmentation of CT hand scans into the finger bones. The task can be applied to right and and left hand scans. The pipeline consist of the following steps:

• Step 1: Perform image inhomogeneity correction of the CT scan via N4ITKBiasFieldCorrection (Tustison et al 2010)
• Step 2: Register the atlas to the CT scan via BRAINSFit (Johnson et al 2007)
  • Step 2a:
  • Step 2b:
  • Step 2c:
• Step 3: Compute the intensity distributions for each structure
  

Compute intensity distribution (mean and variance) for each label by automatically sampling from the MR scan. The sampling for a specific label is constrained to the region that consists of voxels with high probability (top 95%) of being assigned to the label according to the aligned atlas.

• Step 4: Automatically segment the CT scan into the structures of interest using EM Algorithm (Pohl et al 2007)

Anatomical Tree

The anatomical tree represents the structures to be segmented. Node labels displayed below contain a human readable structure name and in parentheses the internally used structure name.

Current Tree:

• Hand
  • Distal1
    • TissueD1
    • BoneD1
  • Proximal1
    • TissueP1
    • BoneP1
  • Medial1
    • TissueM1
    • BoneM1
  • Distal2
    • TissueD2
    • BoneD2
  • Proximal2
    • TissueP2
    • BoneP2
  • Medial2
    • TissueM2
    • BoneM2
  • Distal3
    • TissueD3
    • BoneD3
  • Proximal3
    • TissueP3
    • BoneP3
  • Medial3
    • TissueM3
    • BoneM3
  • Distal4
    • TissueD4
    • BoneD4
  • Proximal4
    • TissueP4
    • BoneP4
  • Medial4
    • TissueM4
    • BoneM4
  • Tissue
  • Air

Proposed Tree for all 5 finger

• Hand
  • Air/Background
  • Tissue
  • Bone
    • Distal1
    • Proximal1
    • Medial1
    • Distal2
    • Proximal2
    • Medial2
    • Distal3
    • Proximal3
    • Medial3
    • Distal4
    • Proximal4
    • Medial4
    • Distal5
    • Proximal5
    • Medial5

Collaborators

Vincent Magnotta (University of Iowa)

Acknowledgment

The construction of the pipeline was supported by funding from NIH NCRR 2P41RR013218 Supplement.

Citations

• Tustison NJ, Avants BB, Cook PA, Zheng Y, Egan A, Yushkevich PA, Gee JC N4ITK: Improved N3 Bias Correction, IEEE Trans Med Imag, 2010
• Pohl K, Bouix S, Nakamura M, Rohlfing T, McCarley R, Kikinis R, Grimson W, Shenton M, Wells W. A Hierarchical Algorithm for MR Brain Image Parcellation. IEEE Transactions on Medical Imaging. 2007 Sept;26(9):1201-1212.
• S. Warfield, J. Rexilius, P. Huppi, T. Inder, E. Miller, W. Wells, G. Zientara, F. Jolesz, and R. Kikinis, “A binary entropy measure to assess nonrigid registration algorithms,” in MICCAI, LNCS, pp. 266–274, Springer, October 2001.
• Johnson H.J., Harris G., Williams K. BRAINSFit: Mutual Information Registrations of Whole-Brain 3D Images, Using the Insight Toolkit, The Insight Journal, July 2007
• T. Vercauteren, X. Pennec, A. Perchant, N. Ayache. Symmetric Log-Domain Diffeomorphic Registration: A Demons-based Approach. MICCAI 2008