A spatiotemporal atlas of MR intensity, tissue probability and shape of the fetal brain with application to segmentation

被引:124
作者
Habas, Piotr A. [1 ,2 ]
Kim, Kio [1 ,2 ]
Corbett-Detig, James M. [1 ,2 ]
Rousseau, Francois [3 ]
Glenn, Orit A. [1 ]
Barkovich, A. James [1 ]
Studholme, Colin [1 ,2 ]
机构
[1] Univ Calif San Francisco, Dept Radiol & Biomed Imaging, San Francisco, CA 94143 USA
[2] Univ Calif San Francisco, Biomed Image Comp Grp, San Francisco, CA 94143 USA
[3] Univ Strasbourg, CNRS, LSIIT, UMR 7005, F-67400 Illkirch Graffenstaden, France
基金
欧洲研究理事会;
关键词
Structural MRI; Fetal imaging; Atlas building; Tissue segmentation; AUTOMATIC SEGMENTATION; GERMINAL MATRIX; IMAGES; RECONSTRUCTION; REGISTRATION; MODEL;
D O I
10.1016/j.neuroimage.2010.06.054
中图分类号
Q189 [神经科学];
学科分类号
071006 ;
摘要
Modeling and analysis of MR Images of the developing human brain is a challenge due to rapid changes in brain morphology and morphometry. We present an approach to the construction of a spatiotemporal atlas of the fetal brain with temporal models of MR intensity, tissue probability and shape changes This spatiotemporal model is created from a set of reconstructed MR images of fetal subjects with different gestational ages Groupwise registration of manual segmentations and voxelwise nonlinear modeling allow us to capture the appearance, disappearance and spatial variation of brain structures over time Applying this model to atlas-based segmentation, we generate age-specific MR templates and tissue probability maps and use them to initialize automatic tissue delineation in new MR images The choice of model parameters and the final performance are evaluated using clinical MR scans of young fetuses with gestational ages ranging from 20 57 to 24 71 weeks. Experimental results indicate that quadratic temporal models can correctly capture growth-related changes in the fetal brain anatomy and provide improvement in accuracy of atlas-based tissue segmentation (C) 2010 Elsevier Inc All rights reserved
引用
收藏
页码:460 / 470
页数:11
相关论文
共 34 条
[1]   Unified segmentation [J].
Ashburner, J ;
Friston, KJ .
NEUROIMAGE, 2005, 26 (03) :839-851
[2]   A COMPUTERIZED SYSTEM FOR THE ELASTIC MATCHING OF DEFORMED RADIOGRAPHIC IMAGES TO IDEALIZED ATLAS IMAGES [J].
BAJCSY, R ;
LIEBERSON, R ;
REIVICH, M .
JOURNAL OF COMPUTER ASSISTED TOMOGRAPHY, 1983, 7 (04) :618-625
[3]   Magnetic resonance imaging of the brain in very preterm infants: Visualization of the germinal matrix, early myelination, and cortical folding [J].
Battin, MR ;
Maalouf, EF ;
Counsell, SJ ;
Herlihy, AH ;
Rutherford, MA ;
Azzopardi, D ;
Edwards, AD .
PEDIATRICS, 1998, 101 (06) :957-962
[4]  
Davis Benjamin C., 2007, Oceans 2007, P1
[5]   MEASURES OF THE AMOUNT OF ECOLOGIC ASSOCIATION BETWEEN SPECIES [J].
DICE, LR .
ECOLOGY, 1945, 26 (03) :297-302
[6]  
GIRARD N, 1995, AM J NEURORADIOL, V16, P407
[7]   MR imaging of the fetal brain [J].
Glenn, Orit A. .
PEDIATRIC RADIOLOGY, 2010, 40 (01) :68-81
[8]   Atlas-Based Segmentation of Developing Tissues in the Human Brain with Quantitative Validation in Young Fetuses [J].
Habas, Piotr A. ;
Kim, Kio ;
Rousseau, Francois ;
Glenn, Orit A. ;
Barkovich, A. James ;
Studholme, Colin .
HUMAN BRAIN MAPPING, 2010, 31 (09) :1348-1358
[9]  
Heller EN, 2001, CAN J CARDIOL, V17, P309
[10]   MRI of moving subjects using multislice snapshot images with volume reconstruction (SVR): Application to fetal, neonatal, and adult brain studies [J].
Jiang, Shuzhou ;
Xue, Hui ;
Glover, Alan ;
Rutherford, Mary ;
Rueckert, Daniel ;
Hajnal, Joseph V. .
IEEE TRANSACTIONS ON MEDICAL IMAGING, 2007, 26 (07) :967-980