Abstract
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 patch-based segmentation.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Aljabar, P., Heckemann, R.A., Hammers, A., Hajnal, J.V., Rueckert, D.: Multi-atlas based segmentation of brain images: atlas selection and its effect on accuracy. NeuroImage 46(3), 726–738 (2009)
Cardoso, M.J., Wolz, R., Modat, M., Fox, N.C., Rueckert, D., Ourselin, S.: Geodesic information flows. In: Ayache, N., Delingette, H., Golland, P., Mori, K. (eds.) MICCAI 2012, Part II. LNCS, vol. 7511, pp. 262–270. Springer, Heidelberg (2012)
Coupé, P., Manjón, J.V., Fonov, V., Pruessner, J., Robles, M., Collins, D.L.: Patch-based segmentation using expert priors: application to hippocampus and ventricle segmentation. NeuroImage 54(2), 940–954 (2011)
Habas, P.A., Kim, K., Rousseau, F., Glenn, O.A., Barkovich, A.J., Studholme, C.: Atlas-based segmentation of developing tissues in the human brain with quantitative validation in young fetuses. Hum. Brain Mapp. 31(9), 1348–1358 (2010)
Heckemann, R.A., Hajnal, J.V., Aljabar, P., Rueckert, D., Hammers, A.: Automatic anatomical brain MRI segmentation combining label propagation and decision fusion. NeuroImage 33(1), 115–126 (2006)
Jiang, S., Xue, H., Glover, A., Rutherford, M., Rueckert, D., Hajnal, J.V.: MRI of moving subjects using multislice snapshot images with volume reconstruction. IEEE Trans. Med. Imag. 26(7), 967–980 (2007)
Kuettel, D., Guillaumin, M., Ferrari, V.: Segmentation propagation in ImageNet. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part VII. LNCS, vol. 7578, pp. 459–473. Springer, Heidelberg (2012)
Prayer, D., Kasprian, G., Krampl, E., Ulm, B., Witzani, L., Prayer, L., Brugger, P.: MRI of normal fetal brain development. Eur. J. Radiol. 57(2), 199–216 (2006)
Rubinstein, M., Liu, C., Freeman, W.T.: Annotation propagation in large image databases via dense image correspondence. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part III. LNCS, vol. 7574, pp. 85–99. Springer, Heidelberg (2012)
Wolz, R., Aljabar, P., Hajnal, J.V., Hammers, A., Rueckert, D.: LEAP: learning embeddings for atlas propagation. NeuroImage 49(2), 1316–1325 (2010)
Wright, R., Vatansever, D., Kyriakopoulou, V.: Age dependent fetal MR segmentation using manual and automated approaches. In: MICCAI 2012 PaPI Workshop, pp. 97–104 (2012)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Koch, L.M. et al. (2014). Graph-Based Label Propagation in Fetal Brain MR Images. In: Wu, G., Zhang, D., Zhou, L. (eds) Machine Learning in Medical Imaging. MLMI 2014. Lecture Notes in Computer Science, vol 8679. Springer, Cham. https://doi.org/10.1007/978-3-319-10581-9_2
Download citation
DOI: https://doi.org/10.1007/978-3-319-10581-9_2
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-10580-2
Online ISBN: 978-3-319-10581-9
eBook Packages: Computer ScienceComputer Science (R0)