Pelvis Segmentation Using Multi-pass U-Net and Iterative Shape Estimation

  • Chunliang WangEmail author
  • Bryan Connolly
  • Pedro Filipe de Oliveira Lopes
  • Alejandro F. Frangi
  • Örjan Smedby
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11404)


In this report, an automatic method for segmentation of the pelvis in three-dimensional (3D) computed tomography (CT) images is proposed. The method is based on a 3D U-net which has as input the 3D CT image and estimated volumetric shape models of the targeted structures and which returns the probability maps of each structure. During training, the 3D U-net is initially trained using blank shape context inputs to generate the segmentation masks, i.e. relying only on the image channel of the input. The preliminary segmentation results are used to estimate a new shape model, which is then fed to the same network again, with the input images. With the additional shape context information, the U-net is trained again to generate better segmentation results. During the testing phase, the input image is fed through the same 3D U-net multiple times, first with blank shape context channels and then with iteratively re-estimated shape models. Preliminary results show that the proposed multi-pass U-net with iterative shape estimation outperforms both 2D and 3D conventional U-nets without the shape model.


Deep learning Multi-pass U-net Pelvis segmentation Shape context Statistic shape model 



This study was supported by the Swedish Heart-lung foundation (grant no. 20160609), Swedish Medtech4Health AIDA research grant, and the Swedish Childhood Cancer Foundation (grant no. MT2016-00166).


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Chunliang Wang
    • 1
    Email author
  • Bryan Connolly
    • 2
  • Pedro Filipe de Oliveira Lopes
    • 3
  • Alejandro F. Frangi
    • 3
  • Örjan Smedby
    • 1
  1. 1.Department of Biomedical Engineering and Health SystemsKTH Royal Institute of TechnologyStockholmSweden
  2. 2.Radiology DepartmentKarolinska InstituteSolnaSweden
  3. 3.Center for Computational Imaging and Simulation Technologies in BiomedicineThe University of SheffieldSheffieldUK

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