Motion Corrected 3D Reconstruction of the Fetal Thorax from Prenatal MRI

  • Bernhard Kainz
  • Christina Malamateniou
  • Maria Murgasova
  • Kevin Keraudren
  • Mary Rutherford
  • Joseph V. Hajnal
  • Daniel Rueckert
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8674)


In this paper we present a semi-automatic method for analysis of the fetal thorax in genuine three-dimensional volumes. After one initial click we localize the spine and accurately determine the volume of the fetal lung from high resolution volumetric images reconstructed from motion corrupted prenatal Magnetic Resonance Imaging (MRI). We compare the current state-of-the-art method of segmenting the lung in a slice-by-slice manner with the most recent multi-scan reconstruction methods. We use fast rotation invariant spherical harmonics image descriptors with Classification Forest ensemble learning methods to extract the spinal cord and show an efficient way to generate a segmentation prior for the fetal lung from this information for two different MRI field strengths. The spinal cord can be segmented with a DICE coefficient of 0.89 and the automatic lung segmentation has been evaluated with a DICE coefficient of 0.87. We evaluate our method on 29 fetuses with a gestational age (GA) between 20 and 38 weeks and show that our computed segmentations and the manual ground truth correlate well with the recorded values in literature.


Fetal Lung Fetal Magnetic Resonance Image Ground Truth Segmentation Fetal Lung Development Total Variation Denoising 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Bernhard Kainz
    • 1
  • Christina Malamateniou
    • 2
  • Maria Murgasova
    • 2
  • Kevin Keraudren
    • 1
  • Mary Rutherford
    • 2
  • Joseph V. Hajnal
    • 2
  • Daniel Rueckert
    • 1
  1. 1.Imperial College LondonDepartment of ComputingLondonUK
  2. 2.Division of Imaging SciencesKing’s College LondonLondonUK

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