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Automatic Whole Heart Segmentation in CT Images Based on Multi-atlas Image Registration

  • Guanyu Yang
  • Chenchen Sun
  • Yang Chen
  • Lijun Tang
  • Huazhong Shu
  • Jean-louis Dillenseger
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10663)

Abstract

Whole heart segmentation in CT images is a significant prerequisite for clinical diagnosis or treatment. In this work, we present a three-step multi-atlas-based method for obtaining a segmentation of the whole heart. In the first step, the region of the heart was detected by aligning the down-sampled patient CT with the low-resolution atlas images. The detected region of heart was used to crop the original patient image. In the second step, the registration between high-resolution atlas images and cropped original patient images was performed to obtain the precise segmentation of the heart. In the third step, the registration was performed again by minimizing the dissimilarity within the heart region. Finally, the labels of four cardiac chambers, aorta and pulmonary artery were generated according to the similarity between the deformed atlas images and the patient image. A leave-one-out experiment has been performed on the 20 training datasets of MM-WHS 2017 challenge. The average Dice coefficient between our segmentation results and the manual segmentation results is 0.9051. The mean and standard deviation of Dice coefficients of each structure (i.e. LV, RV, LA, RA, Myo, Ao, PA) are 0.9601 ± 0.0324, 0.9344 ± 0.0418, 0.9594 ± 0.0316, 0.8836 ± 0.0826, 0.8724 ± 0.0707, 0.9295 ± 0.0883, 0.7966 ± 0.1149 respectively.

Keywords

Whole heart segmentation Heart Segmentation Multi-atlas Cardiac chambers 

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Guanyu Yang
    • 1
    • 4
  • Chenchen Sun
    • 1
    • 4
  • Yang Chen
    • 1
    • 4
  • Lijun Tang
    • 3
  • Huazhong Shu
    • 1
    • 4
  • Jean-louis Dillenseger
    • 2
    • 4
  1. 1.Lab of Image Science and Technology, School of Computer Science and EngineeringSoutheast UniversityNanjingChina
  2. 2.INSERM-U1099, LTSI, Université de Rennes 1RennesFrance
  3. 3.Department of Radiology, The First Affiliated Hospital of Nanjing Medical UniversityNanjingChina
  4. 4.Centre de Recherche en Information Biomedicale Sino-Francais (CRIBs)NanjingChina

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