Keraudren K, Kyriakopoulou V, Rutherford M, Hajnal JV, Rueckert D (2013)
Publication Type: Conference contribution
Publication year: 2013
Book Volume: 8149 LNCS
Pages Range: 582-589
Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Event location: JPN
ISBN: 9783642408106
DOI: 10.1007/978-3-642-40811-3_73
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. © 2013 Springer-Verlag.
APA:
Keraudren, K., Kyriakopoulou, V., Rutherford, M., Hajnal, J.V., & Rueckert, D. (2013). Localisation of the brain in fetal MRI using bundled SIFT features. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 582-589). JPN.
MLA:
Keraudren, Kevin, et al. "Localisation of the brain in fetal MRI using bundled SIFT features." Proceedings of the 16th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2013, JPN 2013. 582-589.
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