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Impact of the Number of Atlases in a Level Set Formulation of Multi-atlas Segmentation

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Advances in Visual Computing (ISVC 2015)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 9474))

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Abstract

In this paper, we present a multi-atlas segmentation method based on the level set formulation for performing label fusion that takes into account the image information and regularity of the region of interest (ROI). In the presented method, multiple atlases are first registered to a target image by deformable registration via attribute matching and mutual saliency weighting (DRAMMS) and advanced neuroimaging tools (ANTs) to get the warped labels. Then, an optimal labeling is sought by label fusion for segmentation of target image. Label fusion is achieved by seeking an optimal level set function which minimizes an energy functional in regards to three terms: label fusion term, image based term, and regularization term. In this work, we discussed the impact of subset on the accuracy of segment results. Results show that segmentation results will be much more accurate if an appropriate subset of atlases are selected for each target image than those given by non-selective combination of random atlas subsets.

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Acknowledgment

This research was partly supported by National Natural Science Foundation of China (NSFC) under Grant No. 61302012 and No. 61172002, the Fundamental Research Funds for the Central Universities under Grant N130418002, N120518001, N140403006 and N140402003. The National Key Technology support Program 2014BAI17B02, and Liaoning Natural Science Foundation under Grant No. 2013020021.

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Authors and Affiliations

  1. No Institute Key Laboratory of Medical Image Computing Ministry of Education, Northeastern University, Shenyang, China

    Yihua Song, Zhaoxuan Gong, Dazhe Zhao & Chaolu Feng

  2. University of Electronic Science and Technology of China, Chengdu, China

    Chunming Li

  3. Department of Radiology, University of Pennsylvania, Philadelphia, USA

    Yihua Song & Chunming Li

Authors
  1. Yihua Song
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  2. Zhaoxuan Gong
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  3. Dazhe Zhao
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  4. Chaolu Feng
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  5. Chunming Li
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Corresponding author

Correspondence to Chunming Li .

Editor information

Editors and Affiliations

  1. University of Nevada, Reno, Nevada, USA

    George Bebis

  2. NASA Ames Research Center, Moffett Field, California, USA

    Richard Boyle

  3. Lawrence Berkeley National Laboratory, Berkeley, California, USA

    Bahram Parvin

  4. Desert Research Institute, Reno, Nevada, USA

    Darko Koracin

  5. University of Houston, Houston, Texas, USA

    Ioannis Pavlidis

  6. IBM T.J. Watson Research Center, Yorktown Heights, New York, USA

    Rogerio Feris

  7. Purdue University, West Lafayette, Indiana, USA

    Tim McGraw

  8. Side Effects Software, Santa Monica, California, USA

    Mark Elendt

  9. The DiVE, Durham, North Carolina, USA

    Regis Kopper

  10. Texas A&M University, College Station, Texas, USA

    Eric Ragan

  11. Kent State University, Kent, Ohio, USA

    Zhao Ye

  12. Lawrence Berkeley National Laboratory, Berkeley, California, USA

    Gunther Weber

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Song, Y., Gong, Z., Zhao, D., Feng, C., Li, C. (2015). Impact of the Number of Atlases in a Level Set Formulation of Multi-atlas Segmentation. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2015. Lecture Notes in Computer Science(), vol 9474. Springer, Cham. https://doi.org/10.1007/978-3-319-27857-5_48

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  • DOI: https://doi.org/10.1007/978-3-319-27857-5_48

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-27856-8

  • Online ISBN: 978-3-319-27857-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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