@inbook{11cd99ac3eb344179578eed7d7a4ef35,
title = "Localisation of the brain in fetal MRI using bundled SIFT features.",
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.",
keywords = "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",
author = "Kevin Keraudren and Vanessa Kyriakopoulou and Mary Rutherford and Hajnal, {Joseph V.} and Daniel Rueckert",
year = "2013",
month = sep,
doi = "10.1007/978-3-642-40811-3_73",
language = "English",
isbn = "978-3-642-40810-6",
volume = "16",
series = "Lecture Notes in Computer Science",
publisher = "Springer Berlin Heidelberg",
pages = "582--589",
editor = "Mori, {Kensaku } and Ichiro Sakuma and Yoshinobu Sato and Barillot, {Christian } and Nassir Navab",
booktitle = "Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention",
edition = "Pt 1",
}