Literature DB >> 29702415

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

Tarun Gangwar1, Jeff Calder2, Takashi Takahashi3, Joan E Bechtold4, Dominik Schillinger5.   

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. Published by Elsevier B.V.

Entities:  

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

Mesh:

Year:  2018        PMID: 29702415     DOI: 10.1016/j.media.2018.04.003

Source DB:  PubMed          Journal:  Med Image Anal        ISSN: 1361-8415            Impact factor:   8.545


  2 in total

1.  Automated Fractured Bone Segmentation and Labeling from CT Images.

Authors:  Darshan D Ruikar; K C Santosh; Ravindra S Hegadi
Journal:  J Med Syst       Date:  2019-02-02       Impact factor: 4.460

2.  Comparison of cartilage and bone morphological models of the ankle joint derived from different medical imaging technologies.

Authors:  Gilda Durastanti; Alberto Leardini; Sorin Siegler; Stefano Durante; Alberto Bazzocchi; Claudio Belvedere
Journal:  Quant Imaging Med Surg       Date:  2019-08
  2 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.