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
 · 
142 Views
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: In fetal brain MRI, most of the high-resolution reconstruction algorithms rely on brain segmentation as a preprocessing step. Manual brain segmentation is however highly time-consuming and therefore not a realistic solution. In this work, we assess on a large dataset the performance of Multiple Atlas Fusion (MAF) strategies to automatically address this problem. Firstly, we show that MAF significantly increase the accuracy of brain segmentation as regards single-atlas strategy. Secondly, we show that MAF compares favourably with the most recent approach (Dice above 0.90). Finally, we show that MAF could in turn provide an enhancement in terms of reconstruction quality.
    SPIE Medical Imaging, Orlando; 02/2015
  • 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
  • [Show abstract] [Hide abstract]
    ABSTRACT: Fetal dosimetry studies require the development of accurate numerical 3D models of the pregnant woman and the fetus. This paper proposes a 3D articulated fetal growth model covering the main phases of pregnancy and a pregnant woman model combining the utero-fetal structures and a deformable non-pregnant woman body envelope. The structures of interest were automatically or semi-automatically (depending on the stage of pregnancy) segmented from a database of images and surface meshes were generated. By interpolating linearly between fetal structures, each one can be generated at any age and in any position. A method is also described to insert the utero-fetal structures in the maternal body. A validation of the fetal models is proposed, comparing a set of biometric measurements to medical reference charts. The usability of the pregnant woman model in dosimetry studies is also investigated, with respect to the influence of the abdominal fat layer.
    Physics in Medicine and Biology 07/2014; 59(16):4583. DOI:10.1088/0031-9155/59/16/4583 · 2.92 Impact Factor

Preview

Download
3 Downloads
Available from