Localisation of the Brain in Fetal MRI Using Bundled SIFT Features

  • Kevin Keraudren
  • Vanessa Kyriakopoulou
  • Mary Rutherford
  • Joseph V. Hajnal
  • Daniel Rueckert
Conference paper

DOI: 10.1007/978-3-642-40811-3_73

Volume 8149 of the book series Lecture Notes in Computer Science (LNCS)
Cite this paper as:
Keraudren K., Kyriakopoulou V., Rutherford M., Hajnal J.V., Rueckert D. (2013) Localisation of the Brain in Fetal MRI Using Bundled SIFT Features. In: Mori K., Sakuma I., Sato Y., Barillot C., Navab N. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2013. MICCAI 2013. Lecture Notes in Computer Science, vol 8149. Springer, Berlin, Heidelberg

Abstract

Fetal MRI is a rapidly emerging diagnostic imaging tool. Its main focus is currently on brain imaging, but there is a huge potential for whole body studies. We propose a method for accurate and robust localisation of the fetal brain in MRI when the image data is acquired as a stack of 2D slices misaligned due to fetal motion. We first detect possible brain locations in 2D images with a Bag-of-Words model using SIFT features aggregated within Maximally Stable Extremal Regions (called bundled SIFT), followed by a robust fitting of an axis-aligned 3D box to the selected regions. We rely on prior knowledge of the fetal brain development to define size and shape constraints. In a cross-validation experiment, we obtained a median error distance of 5.7mm from the ground truth and no missed detection on a database of 59 fetuses. This 2D approach thus allows a robust detection even in the presence of substantial fetal motion.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Kevin Keraudren
    • 1
  • Vanessa Kyriakopoulou
    • 2
  • Mary Rutherford
    • 2
  • Joseph V. Hajnal
    • 2
  • Daniel Rueckert
    • 1
  1. 1.Biomedical Image Analysis GroupImperial College LondonUK
  2. 2.Centre for the Developing Brain & Department Biomedical Engineering Division of Imaging SciencesKing’s College LondonUK