Vessel tractography using an intensity based tensor model with branch detection

Suheyla Cetin*, Ali Demir, Anthony Yezzi, Muzaffer Degertekin, Gozde Unal

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

64 Citations (Scopus)

Abstract

In this paper, we present a tubular structure segmentation method that utilizes a second order tensor constructed from directional intensity measurements, which is inspired from diffusion tensor image (DTI) modeling. The constructed anisotropic tensor which is fit inside a vessel drives the segmentation analogously to a tractography approach in DTI. Our model is initialized at a single seed point and is capable of capturing whole vessel trees by an automatic branch detection algorithm developed in the same framework. The centerline of the vessel as well as its thickness is extracted. Performance results within the Rotterdam Coronary Artery Algorithm Evaluation framework are provided for comparison with existing techniques. 96.4% average overlap with ground truth delineated by experts is obtained in addition to other measures reported in the paper. Moreover, we demonstrate further quantitative results over synthetic vascular datasets, and we provide quantitative experiments for branch detection on patient computed tomography angiography (CTA) volumes, as well as qualitative evaluations on the same CTA datasets, from visual scores by a cardiologist expert.

Original languageEnglish
Article number6352918
Pages (from-to)348-363
Number of pages16
JournalIEEE Transactions on Medical Imaging
Volume32
Issue number2
DOIs
Publication statusPublished - 2013
Externally publishedYes

Keywords

  • Branch detection
  • computed tomography angiography (CTA)
  • coronary arteries
  • segmentation
  • tensor estimation
  • tractography
  • tubular structures
  • vessel trees

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