Dissemin is shutting down on January 1st, 2025

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Springer Verlag, Lecture Notes in Computer Science, p. 582-589

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

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

This paper is available in a repository.
This paper is available in a repository.

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Preprint: archiving allowed
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Postprint: archiving allowed
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Data provided by SHERPA/RoMEO

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.