Left Atrial Appendage Segmentation Based on Ranking 2-D Segmentation Proposals

  • Lei Wang
  • Jianjiang Feng
  • Cheng Jin
  • Jiwen Lu
  • Jie Zhou
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10124)


The left atrial appendage (LAA) is the main source of thrombus in patients with atrial fibrillation (AF). Automated segmentation of the LAA can greatly help doctors diagnose thrombosis and plan LAA closure surgery. Considering large anatomical variations of the LAA, we present a non-model based semi-automated approach for LAA segmentation on CTA data. The method requires only manual selection of four fiducial points to obtain the bounding box for the LAA. Subsequently we generate a pool of segmentation proposals using parametric max-flow for each 2-D slice. Then a random forest regressor is trained to pick out the best 2-D proposal for each slice. Finally all selected 2-D proposals are merged into a 3-D model using spatial continuity. Experimental results on 60 CTA data showed that our approach was robust when dealing with large anatomical variations. Compared to manual annotation, we obtained an average dice overlap of 95.12%.


Left atrial appendage LAA closure surgery Non-model based segmentation Ranking 



This work is supported by the National Natural Science Foundation of China under Grants 61225008, 61373074, 61572271, 61527808 and 61373090, the National Basic Research Program of China under Grant 2014CB349304, the Ministry of Education of China under Grant 20120002110033, and the Tsinghua University Initiative Scientific Research Program.


  1. 1.
    Rosendaal, F.R., Raskob, G.E.: On world thrombosis day. The Lancet 384(9955), 1653–1654 (2014)CrossRefGoogle Scholar
  2. 2.
    Patti, G., Pengo, V., et al. The left atrial appendage: from embryology to prevention of thromboembolism. Eur. Heart J. doi: Epub 2016 Apr 26
  3. 3.
    Wang, Y., Di Biase, L., et al.: Left atrial appendage studied by computed tomography to help planning for appendage closure device placement. J. Cardiovasc. Electrophysiol. 21(9), 973–982 (2010)CrossRefGoogle Scholar
  4. 4.
    Grasland-Mongrain, P., Peters, J., Ecabert, O.: Combination of shape-constrained and inflation deformable models with application to the segmentation of the left atrial appendage. In: ISBI, pp. 428–431 (2010)Google Scholar
  5. 5.
    Grasland-Mongrain, P.: Segmentation of the left atrial appendage from 3D images. Master Thesis. ENS Cachan (2009)Google Scholar
  6. 6.
    Ecabert, O., Peters, J., Schramm, H., Lorenz, C., et al.: Automatic model-based segmentation of the heart in CT images. IEEE Trans. Med. Imaging 27(9), 1189–1201 (2008)CrossRefGoogle Scholar
  7. 7.
    Zheng, Y., Yang, D., John, M., Comaniciu, D.: Multi-part modeling and segmentation of left atrium in C-arm CT for image-guided ablation of atrial fibrillation. IEEE Trans. Med. Imaging 33(2), 318–331 (2014)CrossRefGoogle Scholar
  8. 8.
    Boykov, Y., Funka-Lea, G.: Graph cuts and efficient N-D image segmentation. Int. J. Comput. Vis. 70(2), 109–131 (2006)CrossRefGoogle Scholar
  9. 9.
    Kolmogorov, V., Boykov, Y., Rother, C.: Applications of parametric maxflow in computer vision. In: ICCV 2007, pp. 1–8 (2007)Google Scholar
  10. 10.
    Carreira, J., Sminchisescu, C.: CPMC: automatic object segmentation using constrained parametric min-cuts. IEEE Trans. Pattern Anal. Mach. Intell. 34(7), 1312–1328 (2012)CrossRefGoogle Scholar
  11. 11.
    Hochbaum, D.S.: The pseudoflow algorithm: a new algorithm for the maximum-flow problem. Oper. Res. 56(4), 992–1009 (2008)MathSciNetCrossRefMATHGoogle Scholar
  12. 12.
    Carreira, J., Sminchisescu, C.: Constrained parametric min-cuts for automatic object segmentation, release 1.
  13. 13.
    Wertheimer, M.: Laws of organization in perceptual forms (partial translation). In: A Source-Book of Gestalt Psycychology, pp. 71–88 (1938)Google Scholar
  14. 14.
    Fedorov, A., Beichel, R., et al.: 3D slicer as an image computing platform for the quantitative imaging network. Magn. Reson. Imaging 30(9), 1323–1341 (2012)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Lei Wang
    • 1
  • Jianjiang Feng
    • 1
  • Cheng Jin
    • 1
  • Jiwen Lu
    • 1
  • Jie Zhou
    • 1
  1. 1.Tsinghua National Laboratory for Information Science and Technology Department of AutomationTsinghua UniversityBeijingChina

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