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Coronary Centerline Extraction via Optimal Flow Paths and CNN Path Pruning

  • Mehmet A. GülsünEmail author
  • Gareth Funka-Lea
  • Puneet Sharma
  • Saikiran Rapaka
  • Yefeng Zheng
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9902)

Abstract

We present a novel method for the automated extraction of blood vessel centerlines. There are two major contributions. First, in order to avoid the shortcuts to which minimal path methods are prone, we find optimal paths in a computed flow field. We solve for a steady state porous media flow inside a region of interest and trace centerlines as maximum flow paths. We explain how to estimate anisotropic orientation tensors which are used as permeability tensors in our flow field computation. Second, we introduce a convolutional neural network (CNN) classifier for removing extraneous paths in the detected centerlines. We apply our method to the extraction of coronary artery centerlines found in Computed Tomography Angiography (CTA). The robustness and stability of our method are enhanced by using a model-based detection of coronary specific territories and main branches to constrain the search space [15]. Validation against 20 comprehensively annotated datasets had a sensitivity and specificity at or above 90 %. Validation against 106 clinically annotated coronary arteries showed a sensitivity above 97 %.

Notes

Acknowledgment

The authors thank Adriaan Coenen, MD at Erasmus Univ. Medical Center for processing and making available the clinical data set.

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© Springer International Publishing AG 2016

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Authors and Affiliations

  • Mehmet A. Gülsün
    • 1
    Email author
  • Gareth Funka-Lea
    • 1
  • Puneet Sharma
    • 1
  • Saikiran Rapaka
    • 1
  • Yefeng Zheng
    • 1
  1. 1.Medical Imaging Technologies, Siemens Medical Solutions USA, Inc.PrincetonUSA

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