Graph-based label propagation in fetal brain MR images

Lisa M. Koch, Robert Wright, Deniz Vatansever, Vanessa Kyriakopoulou, Christina Malamateniou, Prachi Patkee, Mary Rutherford, Jo Hajnal, Paul Aljabar, Daniel Rueckert, Christina Malamateniou

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

8 Citations (Scopus)


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.

Original languageEnglish
Title of host publicationMachine Learning in Medical Imaging
Subtitle of host publication5th International Workshop, MLMI 2014, Held in Conjunction with MICCAI 2014, Boston, MA, USA, September 14, 2014, Proceedings
EditorsGuorong Wu, Daoqiang Zhang, Luping Zhou
PublisherSpringer International Publishing Switzerland
Number of pages8
ISBN (Electronic)978-3-319-10581-9
ISBN (Print)978-3-319-10580-2
Publication statusPublished - 2014

Publication series

NameLecture Notes in Computer Science
PublisherSpringer International Publishing
ISSN (Print)0302-9743
ISSN (Electronic)0302-9743


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