Robust variational segmentation of 3D bone CT data with thin cartilage interfaces

Tarun Gangwar, Jeff Calder, Takashi Takahashi, Joan E. Bechtold, Dominik Schillinger

Research output: Contribution to journalArticlepeer-review

9 Scopus citations

Abstract

We present a two-stage variational approach for segmenting 3D bone CT data that performs robustly with respect to thin cartilage interfaces. In the first stage, we minimize a flux-augmented Chan–Vese model that accurately segments well-separated regions. In the second stage, we apply a new phase-field fracture inspired model that reliably eliminates spurious bridges across thin cartilage interfaces, resulting in an accurate segmentation topology, from which each bone object can be identified. Its mathematical formulation is based on the phase-field approach to variational fracture, which naturally blends with the variational approach to segmentation. We successfully test and validate our methodology for the segmentation of 3D femur and vertebra bones, which feature thin cartilage regions in the hip joint, the intervertebral disks, and synovial joints of the spinous processes. The major strength of the new methodology is its potential for full automation and seamless integration with downstream predictive bone simulation in a common finite element framework.

Original languageEnglish (US)
Pages (from-to)95-110
Number of pages16
JournalMedical Image Analysis
Volume47
DOIs
StatePublished - Jul 2018

Keywords

  • 3D bone CT data
  • Femur extraction
  • Flux-augmented Chan–Vese model
  • Phase-field fracture mechanics
  • Thin cartilage interfaces
  • Variational segmentation
  • Vertebra extraction
  • Voxel finite elements

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