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Proceedings Paper

Statistical model of laminar structure for atlas-based segmentation of the fetal brain from in utero MR images
Author(s): Piotr A. Habas; Kio Kim; Dharshan Chandramohan; Francois Rousseau; Orit A. Glenn; Colin Studholme
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Paper Abstract

Recent advances in MR and image analysis allow for reconstruction of high-resolution 3D images from clinical in utero scans of the human fetal brain. Automated segmentation of tissue types from MR images (MRI) is a key step in the quantitative analysis of brain development. Conventional atlas-based methods for adult brain segmentation are limited in their ability to accurately delineate complex structures of developing tissues from fetal MRI. In this paper, we formulate a novel geometric representation of the fetal brain aimed at capturing the laminar structure of developing anatomy. The proposed model uses a depth-based encoding of tissue occurrence within the fetal brain and provides an additional anatomical constraint in a form of a laminar prior that can be incorporated into conventional atlas-based EM segmentation. Validation experiments are performed using clinical in utero scans of 5 fetal subjects at gestational ages ranging from 20.5 to 22.5 weeks. Experimental results are evaluated against reference manual segmentations and quantified in terms of Dice similarity coefficient (DSC). The study demonstrates that the use of laminar depth-encoded tissue priors improves both the overall accuracy and precision of fetal brain segmentation. Particular refinement is observed in regions of the parietal and occipital lobes where the DSC index is improved from 0.81 to 0.82 for cortical grey matter, from 0.71 to 0.73 for the germinal matrix, and from 0.81 to 0.87 for white matter.

Paper Details

Date Published: 27 March 2009
PDF: 8 pages
Proc. SPIE 7259, Medical Imaging 2009: Image Processing, 725917 (27 March 2009); doi: 10.1117/12.812425
Show Author Affiliations
Piotr A. Habas, Biomedical Image Computing Group, Univ. of California, San Francisco (United States)
Univ. of California, San Francisco (United States)
Kio Kim, Biomedical Image Computing Group, Univ. of California, San Francisco (United States)
Univ. of California, San Francisco (United States)
Dharshan Chandramohan, Biomedical Image Computing Group, Univ. of California, San Francisco (United States)
Univ. of California, Berkeley (United States)
Francois Rousseau, LSIIT, CNRS/Univ. Louis Pasteur (France)
Orit A. Glenn, Univ. of California, San Francisco (United States)
Colin Studholme, Biomedical Image Computing Group, Univ. of California, San Francisco (United States)
Univ. of California, San Francisco (United States)


Published in SPIE Proceedings Vol. 7259:
Medical Imaging 2009: Image Processing
Josien P. W. Pluim; Benoit M. Dawant, Editor(s)

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