Conference Paper

Automatic segmentation of head structures on fetal MRI

Inst. TELECOM, Telecom ParisTech, Paris, France
DOI: 10.1109/ISBI.2009.5192995 Conference: Biomedical Imaging: From Nano to Macro, 2009. ISBI '09. IEEE International Symposium on
Source: IEEE Xplore

ABSTRACT Recent improvements of fetal MRI acquisitions now allow three-dimensional segmentation of fetal structures, to extract bio-metrical measures for pregnancy follow-up. Automation of the segmentation process remains a difficult challenge, given the complexity of the fetal organs and their spatial organization. As a starting point, we propose in this paper a fully automated segmentation method to localize the eyes and segment the skull bone content (SBC). Priors, embedding contrast, morphological and bio-metrical information, are used to assist the segmentation process. A validation of the proposed segmentation method, on 24 MRI volumes of fetuses between 30 and 35 gestational weeks, demonstrated a high accuracy for eyes and SBC extraction.

1 Bookmark
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: In this paper we present a semi-automatic method for analysis of the fetal thorax in genuine three-dimensional volumes. After one initial click we localize the spine and accurately determine the volume of the fetal lung from high resolution volumetric images reconstructed from motion corrupted prenatal Magnetic Resonance Imaging (MRI). We compare the current state-of-the-art method of segmenting the lung in a slice-by-slice manner with the most recent multi-scan reconstruction methods. We use fast rotation invariant spherical harmonics image descriptors with Classification Forest ensemble learning methods to extract the spinal cord and show an efficient way to generate a segmentation prior for the fetal lung from this information for two different MRI field strengths. The spinal cord can be segmented with a DICE coefficient of 0.89 and the automatic lung segmentation has been evaluated with a DICE coefficient of 0.87. We evaluate our method on 29 fetuses with a gestational age (GA) between 20 and 38 weeks and show that our computed segmentations and the manual ground truth correlate well with the recorded values in literature.
    Medical Image Computing and Computer-Assisted Intervention – MICCAI 2014, Boston, MA; 09/2014
  • Source
    [Show abstract] [Hide abstract]
    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.
    Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention. 01/2013; 16(Pt 1):582-9.
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Fetal magnetic resonance imaging (MRI) is a rapidly growing field of research for its potential to study brain development in utero. However, in contrast to adult studies automatic brain extraction and orientation is not yet solved, but remains challenging in wide field of view raw fetal MRI volumes. This has limited research to small scale studies. This paper presents an automatic fetal brain extraction and orientation framework to remove this limitation. The method consists of a two-phase random forest classifier, and an approximate high-order Markov random field solution, that results in a brain mask for an MRI stack. The resulting extraction achieves 98% detection rate with 88% mean sensitivity when validated on a set of cases aged between 18-30.2 gestational weeks (GW), supporting a robust pipeline to automated fetal MRI processing techniques.
    Perinatal and Paediatric Imaging (PaPI 2012), MICCAI workshop; 10/2012


1 Download
Available from