Abstract
Population level analysis of medical imaging data relies on finding spatial correspondence across individuals as a basis for local comparison of visual characteristics. Here, we describe and evaluate a framework to normalize routine images covering different parts of the human body, in different modalities to a common reference space. The framework performs two basic steps towards normalization: (1) The identification of the location and coverage of the human body by an image and (2) a non-linear mapping to the common reference space. Based on these mappings, either coordinates, or label-masks can be transferred across a population of images. We evaluate the framework on a set of routine CT and MR scans exhibiting large variability on location and coverage. A set of manually annotated landmarks is used to assess the accuracy and stability of the approach. We report distinct improvement in stability and registration accuracy compared to a classical single-atlas approach.
G. Langs—This research was supported by teamplay which is a Digital Health Service of Siemens Healthineers, by the Austrian Science Fund FWF (I2714-B31), by the WWTF (S14-069), and by the DFG (WE 2709/3-1, ME 3511/3-1).
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Crum, W.R., Griffin, L.D., Hill, D.L.G., Hawkes, D.J.: Zen and the art of medical image registration: correspondence, homology, and quality. NeuroImage 20(3), 1425–1437 (2003)
Degen, J., Heinrich, M.P.: Multi-atlas based pseudo-CT synthesis using multimodal image registration and local atlas fusion strategies. In: Computer Vision and Pattern Recognition (CVPR), pp. 160–168 (2016)
Fischl, B., Salat, D.H., Busa, E., Albert, M., Dieterich, M., Haselgrove, C., van der Kouwe, A., Killiany, R., Kennedy, D., Klaveness, S., Montillo, A., Makris, N., Rosen, B., Dale, A.M.: Whole brain segmentation: automated labeling of neuroanatomical structures in the human brain. Neuron 33(3), 341–355 (2002)
Goksel, O., Foncubierta-Rodriguez, A., del Toro, O.A.J., Müller, H., Langs, G., Weber, M.A., Menze, B.H., Eggel, I., Gruenberg, K., et al.: Overview of the VISCERAL challenge at ISBI 2015. In: VISCERAL Challenge@ ISBI, pp. 6–11 (2015)
Gruslys, A., Acosta-Cabronero, J., Nestor, P.J.: Others: a new fast accurate nonlinear medical image registration program including surface preserving regularization. IEEE Trans. Med. Imaging 33(11), 2118–2127 (2014)
Hamm, J., Ye, D.H., Verma, R., Davatzikos, C.: GRAM: a framework for geodesic registration on anatomical manifolds. Med. Image Anal. 14(5), 633–642 (2010)
Heinrich, M.P., Jenkinson, M., Papież, B.W., Brady, S.M., Schnabel, J.A.: Towards realtime multimodal fusion for image-guided interventions using self-similarities. In: Mori, K., Sakuma, I., Sato, Y., Barillot, C., Navab, N. (eds.) MICCAI 2013. LNCS, vol. 8149, pp. 187–194. Springer, Heidelberg (2013). doi:10.1007/978-3-642-40811-3_24
Iglesias, J.E., Sabuncu, M.R.: Multi-atlas segmentation of biomedical images: a survey. Med. Image Anal. 24(1), 205–19 (2015)
Koch, L.M., Rajchl, M., Bai, W., Baumgartner, C.F., Tong, T., Passerat-Palmbach, J., et al.: Multi-Atlas Segmentation using Partially Annotated Data: Methods and Annotation Strategies, pp. 1–17. arXiv preprint, arxiv:1605.00029 (2016)
Toews, M., Wells, W.M.: Efficient and robust model-to-image alignment using 3D scale-invariant features. Med. Image Anal. 17(3), 271–282 (2013)
Viola, P., Wells Iii, W.M.: Alignment by maximization of mutual information. Int. J. Comput. Vis. 9(242), 22–137 (1997)
Wolz, R., Chu, C., Misawa, K., Fujiwara, M., Mori, K., Rueckert, D.: Automated abdominal multi-organ segmentation with subject-specific atlas generation. IEEE Trans. Med. Imaging 32(9), 1723–1730 (2013)
Xie, L., Pluta, J.B., Das, S.R., Wisse, L.E., Wang, H., Mancuso, L., Kliot, D., Avants, B.B., Ding, S.L., Manjón, J.V., Wolk, D.A., Yushkevich, P.A.: Multi-template analysis of human perirhinal cortex in brain MRI: explicitly accounting for anatomical variability. NeuroImage 144, 183–202 (2017)
Zhang, Y., Brady, M., Smith, S.: Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm. IEEE Trans. Med. Imaging 20(1), 45–57 (2001)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Hofmanninger, J., Menze, B., Weber, MA., Langs, G. (2017). Mapping Multi-Modal Routine Imaging Data to a Single Reference via Multiple Templates. In: Cardoso, M., et al. Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support . DLMIA ML-CDS 2017 2017. Lecture Notes in Computer Science(), vol 10553. Springer, Cham. https://doi.org/10.1007/978-3-319-67558-9_39
Download citation
DOI: https://doi.org/10.1007/978-3-319-67558-9_39
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-67557-2
Online ISBN: 978-3-319-67558-9
eBook Packages: Computer ScienceComputer Science (R0)