Automatic Segmentation of Spinal Canals in CT Images via Iterative Topology Refinement

Qian Wang, Le Lu, Dijia Wu, Noha El-Zehiry, Yefeng Zheng, Dinggang Shen, Kevin S. Zhou

Research output: Contribution to journalArticle

11 Citations (Scopus)

Abstract

Accurate segmentation of the spinal canals in computed tomography (CT) images is an important task in many related studies. In this paper, we propose an automatic segmentation method and apply it to our highly challenging image cohort that is acquired from multiple clinical sites and from the CT channel of the PET-CT scans. To this end, we adapt the interactive random-walk solvers to be a fully automatic cascaded pipeline. The automatic segmentation pipeline is initialized with robust voxelwise classification using Haar-like features and probabilistic boosting tree. Then, the topology of the spinal canal is extracted from the tentative segmentation and further refined for the subsequent random-walk solver. In particular, the refined topology leads to improved seeding voxels or boundary conditions, which allow the subsequent random-walk solver to improve the segmentation result. Therefore, by iteratively refining the spinal canal topology and cascading the random-walk solvers, satisfactory segmentation results can be acquired within only a few iterations, even for cases with scoliosis, bone fractures and lesions. Our experiments validate the capability of the proposed method with promising segmentation performance, even though the resolution and the contrast of our dataset with 110 patient cases (90 for testing and 20 for training) are low and various bone pathologies occur frequently.

Original languageEnglish
Article number7112540
Pages (from-to)1694-1704
Number of pages11
JournalIEEE Transactions on Medical Imaging
Volume34
Issue number8
DOIs
Publication statusPublished - 2015 Aug 1
Externally publishedYes

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Keywords

  • Image landmark detection
  • image segmentation
  • random walk
  • spinal canal
  • Topology

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Computer Science Applications
  • Radiological and Ultrasound Technology
  • Software

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