TY - CHAP
T1 - Graph-based label propagation in fetal brain MR images
AU - Koch, Lisa M.
AU - Wright, Robert
AU - Vatansever, Deniz
AU - Kyriakopoulou, Vanessa
AU - Malamateniou, Christina
AU - Patkee, Prachi
AU - Rutherford, Mary
AU - Hajnal, Jo
AU - Aljabar, Paul
AU - Rueckert, Daniel
AU - Malamateniou, Christina
PY - 2014
Y1 - 2014
N2 - Segmentation of neonatal and fetal brain MR images is a challenging task due to vast differences in shape and appearance across age and across subjects. Expert priors for atlas-based segmentation are often only available for a subset of the population, leading to a reduction in accuracy for images dissimilar from the atlas set. To alleviate the effects of limited prior information on atlas-based segmentation, we present a novel semi-supervised learning framework where labels are propagated among both atlas and test images while modelling the confidence of propagated information. The method relies on a voxel-wise graph interconnecting similar regions in all images based on a patch similarity measure. By iteratively allowing information flow from voxels with high confidence to voxels with lower confidence, segmentations in test images with low similarity to the atlas set can be improved. The method was evaluated on 70 fetal brain MR images of subjects at 22–38 weeks gestational age. Particularly for test populations dissimilar from the atlas population, the proposed method outperformed state-of-the-art patchbased segmentation.
AB - Segmentation of neonatal and fetal brain MR images is a challenging task due to vast differences in shape and appearance across age and across subjects. Expert priors for atlas-based segmentation are often only available for a subset of the population, leading to a reduction in accuracy for images dissimilar from the atlas set. To alleviate the effects of limited prior information on atlas-based segmentation, we present a novel semi-supervised learning framework where labels are propagated among both atlas and test images while modelling the confidence of propagated information. The method relies on a voxel-wise graph interconnecting similar regions in all images based on a patch similarity measure. By iteratively allowing information flow from voxels with high confidence to voxels with lower confidence, segmentations in test images with low similarity to the atlas set can be improved. The method was evaluated on 70 fetal brain MR images of subjects at 22–38 weeks gestational age. Particularly for test populations dissimilar from the atlas population, the proposed method outperformed state-of-the-art patchbased segmentation.
UR - http://www.scopus.com/inward/record.url?scp=84921635955&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-10581-9
DO - 10.1007/978-3-319-10581-9
M3 - Chapter
AN - SCOPUS:84921635955
SN - 978-3-319-10580-2
VL - 8679
T3 - Lecture Notes in Computer Science
SP - 9
EP - 16
BT - Machine Learning in Medical Imaging
A2 - Wu, Guorong
A2 - Zhang, Daoqiang
A2 - Zhou, Luping
PB - Springer International Publishing Switzerland
ER -