Localisation of the brain in fetal MRI using bundled SIFT features.

Kevin Keraudren*, Vanessa Kyriakopoulou, Mary Rutherford, Joseph V. Hajnal, Daniel Rueckert

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

26 Citations (Scopus)


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.

Original languageEnglish
Title of host publicationMedical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
Subtitle of host publication16th International Conference, Nagoya, Japan, September 22-26, 2013, Proceedings, Part I
EditorsKensaku Mori, Ichiro Sakuma, Yoshinobu Sato, Christian Barillot, Nassir Navab
Number of pages8
EditionPt 1
ISBN (Electronic)978-3-642-40811-3
Publication statusPublished - Sept 2013

Publication series

NameLecture Notes in Computer Science
PublisherSpringer Berlin Heidelberg
ISSN (Print)0302-9743


  • Algorithms
  • Artificial Intelligence
  • Brain
  • Humans
  • Image Enhancement
  • Image Interpretation, Computer-Assisted
  • Magnetic Resonance Imaging
  • Pattern Recognition, Automated
  • Prenatal Diagnosis
  • Reproducibility of Results
  • Sensitivity and Specificity


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