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International Workshop on Statistical Atlases and Computational Models of the Heart

STACOM 2016: Statistical Atlases and Computational Models of the Heart. Imaging and Modelling Challenges pp 21–29Cite as

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

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Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10124))

Abstract

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%.

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Acknowledgements

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.

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Authors and Affiliations

  1. Tsinghua National Laboratory for Information Science and Technology Department of Automation, Tsinghua University, Beijing, China

    Lei Wang, Jianjiang Feng, Cheng Jin, Jiwen Lu & Jie Zhou

Authors
  1. Lei Wang
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  2. Jianjiang Feng
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  3. Cheng Jin
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  4. Jiwen Lu
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  5. Jie Zhou
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Corresponding author

Correspondence to Jianjiang Feng .

Editor information

Editors and Affiliations

  1. Siemens Medical Solutions, Medical Imaging Technologies, Princeton, New Jersey, USA

    Tommaso Mansi

  2. Simula Research Laboratory, Oslo, Norway

    Kristin McLeod

  3. Sunnybrook Research Institute, University of Toronto, Toronto, Ontario, Canada

    Mihaela Pop

  4. Imaging Sciences and Biomedical Engineering, Rayne Institute, King’s College London, London, United Kingdom

    Kawal Rhode

  5. Inria, Asclepios Project, Sophia-Antipolis, France

    Maxime Sermesant

  6. Department of Radiology, Faculty of Medical and Health Sciences, Auckland University, Auckland, New Zealand

    Alistair Young

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Wang, L., Feng, J., Jin, C., Lu, J., Zhou, J. (2017). Left Atrial Appendage Segmentation Based on Ranking 2-D Segmentation Proposals. In: Mansi, T., McLeod, K., Pop, M., Rhode, K., Sermesant, M., Young, A. (eds) Statistical Atlases and Computational Models of the Heart. Imaging and Modelling Challenges. STACOM 2016. Lecture Notes in Computer Science(), vol 10124. Springer, Cham. https://doi.org/10.1007/978-3-319-52718-5_3

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  • DOI: https://doi.org/10.1007/978-3-319-52718-5_3

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-52717-8

  • Online ISBN: 978-3-319-52718-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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