Multi-part Left Atrium Modeling and Segmentation in C-Arm CT Volumes for Atrial Fibrillation Ablation

  • Yefeng Zheng
  • Tianzhou Wang
  • Matthias John
  • S. Kevin Zhou
  • Jan Boese
  • Dorin Comaniciu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6893)

Abstract

As a minimally invasive surgery to treat left atrial (LA) fibrillation, catheter based ablation uses high radio-frequency energy to eliminate potential sources of the abnormal electrical events, especially around the ostia of pulmonary veins (PV). Due to large structural variations of the PV drainage pattern, a personalized LA model is helpful to translate a generic ablation strategy to a specific patient’s anatomy. Overlaying the LA model onto 2D fluoroscopic images provides valuable visual guidance during surgery. A holistic shape model is not accurate enough to represent the whole shape population of the LA. In this paper, we propose a part based LA model (including the chamber, appendage, and four major PVs) and each part is a much simpler anatomical structure compared to the holistic one. Our approach works on un-gated C-arm CT, where thin boundaries between the LA blood pool and surrounding tissues are often blurred due to the cardiac motion artifacts (which presents a big challenge compared to the highly contrasted gated CT/MRI). To avoid segmentation leakage, the shape prior is exploited in a model based approach to segment the LA parts. However, independent detection of each part is not optimal and its robustness needs further improvement (especially for the appendage and PVs). We propose to enforce a statistical shape constraint during the estimation of pose parameters (position, orientation, and size) of different parts. Our approach is computationally efficient, taking about 1.5 s to process a volume with 256 ×256 ×250 voxels. Experiments on 469 C-arm CT datasets demonstrate its robustness.

Keywords

Pulmonary Vein Left Atrium Atrial Fibrillation Ablation Left Atrium Appendage Segmentation Error 
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-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Yefeng Zheng
    • 1
  • Tianzhou Wang
    • 1
  • Matthias John
    • 2
  • S. Kevin Zhou
    • 1
  • Jan Boese
    • 2
  • Dorin Comaniciu
    • 1
  1. 1.Image Analytics & InformaticsSiemens Corporate ResearchPrincetonUSA
  2. 2.Healthcare SectorSiemens AGForchheimGermany

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