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: Register the atlas to the CT scan via BRAINSFit (Johnson et al 2007)
  • Step 1a:
  • Step 1b:
  • Step 1c:
• Step 2: 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.

Proposed Tree for all 4 finger

• Hand
  • Air
  • Tissue
  • Index finger / digitus secundus (II)
    • Proximal (II)
    • Medial (II)
    • Distal (II)
  • Middle finger / digitus medius (III)
    • Proximal (III)
    • Medial (III)
    • Distal (III)
  • Ring finger / digitus annularis (IV)
    • Proximal (IV)
    • Medial (IV)
    • Distal (IV)
  • Little finger / digitus minimus (V)
    • Proximal (V)
    • Medial (V)
    • Distal (V)

Pre-Processing

• Flip/Mirror the atlas (based on right hand scans) if left hand scan has to be segmented.

• The atlas template scan and the subject scan are processed in the same way.
  • Threshold image (based on Fiducials, TODO)
  • blur image
  • binary image
  • extract largest component

• Register the atlas template linear to the subject scan and save the linear transformation. (BRAINSFit Rigid,Affine)
• Register the atlas template non-linear to the subject scan. Use the linear transformation as initialization. (BRAINSDemonWarp)

Result

• 

Collaborators

Vincent Magnotta (University of Iowa)

Acknowledgment

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

Citations

• 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