International Conference on Medical Image Computing and Computer-Assisted Intervention

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015 pp 29-37 | Cite as

Slic-Seg: Slice-by-Slice Segmentation Propagation of the Placenta in Fetal MRI Using One-Plane Scribbles and Online Learning

  • Guotai Wang
  • Maria A. Zuluaga
  • Rosalind Pratt
  • Michael Aertsen
  • Anna L. David
  • Jan Deprest
  • Tom Vercauteren
  • Sebastien Ourselin
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9351)

Abstract

Segmentation of the placenta from fetal MRI is critical for planning of fetal surgical procedures. Unfortunately, it is made difficult by poor image quality due to sparse acquisition, inter-slice motion, and the widely varying position and orientation of the placenta between pregnant women. We propose a minimally interactive online learning-based method named Slic-Seg to obtain accurate placenta segmentations from MRI. An online random forest is first trained on data coming from scribbles provided by the user in one single selected start slice. This then forms the basis for a slice-by-slice framework that segments subsequent slices before incorporating them into the training set on the fly. The proposed method was compared with its offline counterpart that is with no retraining, and with two other widely used interactive methods. Experiments show that our method 1) has a high performance in the start slice even in cases where sparse scribbles provided by the user lead to poor results with the competitive approaches, 2) has a robust segmentation in subsequent slices, and 3) results in less variability between users.

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Guotai Wang
    • 1
  • Maria A. Zuluaga
    • 1
  • Rosalind Pratt
    • 1
    • 2
  • Michael Aertsen
    • 3
  • Anna L. David
    • 2
  • Jan Deprest
    • 4
  • Tom Vercauteren
    • 1
  • Sebastien Ourselin
    • 1
  1. 1.Translational Imaging Group, CMICUniversity College LondonLondonUK
  2. 2.Institute for Womenś HealthUniversity College LondonLondonUK
  3. 3.Department of RadiologyUniversity Hospitals KU LeuvenLeuvenBelgium
  4. 4.Department of ObstetricsUniversity Hospitals KU LeuvenLeuvenBelgium

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