Sparse Appearance Learning Based Automatic Coronary Sinus Segmentation in CTA

  • Shiyang Lu
  • Xiaojie Huang
  • Zhiyong Wang
  • Yefeng Zheng
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8673)


Interventional cardiologists are often challenged by a high degree of variability in the coronary venous anatomy during coronary sinus cannulation and left ventricular epicardial lead placement for cardiac resynchronization therapy (CRT), making it important to have a precise and fully-automatic segmentation solution for detecting the coronary sinus. A few approaches have been proposed for automatic segmentation of tubular structures utilizing various vesselness measurements. Although working well on contrasted coronary arteries, these methods fail in segmenting the coronary sinus that has almost no contrast in computed tomography angiography (CTA) data, making it difficult to distinguish from surrounding tissues. In this work we propose a multiscale sparse appearance learning based method for estimating vesselness towards automatically extracting the centerlines. Instead of modeling the subtle discrimination at the low-level intensity, we leverage the flexibility of sparse representation to model the inherent spatial coherence of vessel/background appearance and derive a vesselness measurement. After centerline extraction, the coronary sinus lumen is segmented using a learning based boundary detector and Markov random field (MRF) based optimal surface extraction. Quantitative evaluation on a large cardiac CTA dataset (consisting of 204 3D volumes) demonstrates the superior accuracy of the proposed method in both centerline extraction and lumen segmentation, compared to the state-of-the-art.


Cardiac Resynchronization Therapy Coronary Sinus Markov Random Field Lumen Surface Vesselness Measurement 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Shiyang Lu
    • 1
    • 3
  • Xiaojie Huang
    • 2
    • 3
  • Zhiyong Wang
    • 1
  • Yefeng Zheng
    • 3
  1. 1.School of Information TechnologiesThe University of SydneySydneyAustralia
  2. 2.Department of Electrical EngineeringYale UniversityNew HavenUSA
  3. 3.Imaging and Computer VisionSiemens Corporation, Corporate TechnologyPrincetonUSA

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