Abstract
In this work, we present multi-atlas based techniques for both segmentation and landmark detection in images with large field-of-view (FOV). Such images can provide important insight in the anatomical structure of the human body, but are challenging to deal with since the localization search space for landmarks and organs, in addition to the raw amount of data, is large. In many studies, segmentation and localization techniques are developed specifically for an individual target anatomy or image modality. This can leave a substantial amount of the potential of large FOV images untapped, as the co-localization and shape variability of organs are neglected. We thus focus on modality and anatomy independent techniques to be applied to a wide range of input images. For segmentation, we propagate the multi-organ label maps from several atlases to a target image via a large FOV Markov random field (MRF) based non-rigid registration method. The propagated labels are then fused in the target domain using similarity-weighted majority voting. For landmark localization, we use a consensus based fusion of location estimates from several atlases identified by a template-matching approach. We present our results in the IEEE ISBI 2014 VISCERAL challenge as well as VISCERAL Anatomy1 and Anatomy2 benchmarks.
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References
Pekar, V., McNutt, T.R., Kaus, M.R.: Automated model-based organ delineation for radiotherapy planning in prostatic region. Int. J. Radiat. Oncol. Biol. Phys. 60(3), 973–980 (2004)
Goksel, O., Gass, T., Szekely, G.: Segmentation and landmark localization based on multiple atlases. In: Goksel, O. (ed.) Proceedings of the VISCERAL Challenge at ISBI. CEUR Workshop Proceedings, Beijing, China, pp. 37–43, May 2014
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)
Glocker, B., Komodakis, N., Tziritas, G., Navab, N., Paragios, N.: Dense image registration through MRFs and efficient linear programming. Med. Image Anal. 12(6), 731–741 (2008)
Cachier, P., Bardinet, E., Dormont, D., Pennec, X., Ayache, N.: Iconic feature based nonrigid registration: the PASHA algorithm. Comput. Vis. Image Underst. 89(2–3), 272–298 (2003)
Rueckert, D., Aljabar, P., Heckemann, R.a., Hajnal, J.V., Hammers, A.: Diffeomorphic registration using B-splines. In: Proceedings of the Medical Image Computing and Computer-Assisted Intervention, pp. 702–709, January 2006
Kolmogorov, V.: Convergent tree-reweighted message passing for energy minimization. IEEE Trans. Pattern Anal. Mach. Intell. 28, 1568–1583 (2006)
Iglesias, J.E., Karssemeijer, N.: Robust initial detection of landmarks in film-screen mammograms using multiple FFDM atlases. IEEE Trans. Med. Imaging 28(11), 1815–1824 (2009)
Jimenez del Toro, O.A., Goksel, O., Menze, B., Müller, H., Langs, G., Weber, M.A., Eggel, I., Gruenberg, K., Holzer, M., Jakab, A., Kotsios-Kontokotsios, G., Krenn, M., Fernandez, T.S., Schaer, R., Taha, A.A., Winterstein, M., Hanbury, A.: VISCERAL - VISual Concept Extraction challenge in RAdioLogy: ISBI 2014 Challenge Organization. In: VISCERAL Challenge at ISBI, vol. 1194, pp. 6–15 (2014)
Acknowledgements
The research leading to these results has received funding from the European Union Seventh Framework Programme (FP7/2007–2013) under grant agreement n\(^\circ \) 318068. This work has also received funding from the Swiss National Center of Competence in Research on Computer Aided and Image Guided Medical Interventions (NCCR Co-Me) supported by the Swiss National Science Foundation.
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Gass, T., Szekely, G., Goksel, O. (2014). Multi-atlas Segmentation and Landmark Localization in Images with Large Field of View. In: Menze, B., et al. Medical Computer Vision: Algorithms for Big Data. MCV 2014. Lecture Notes in Computer Science(), vol 8848. Springer, Cham. https://doi.org/10.1007/978-3-319-13972-2_16
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DOI: https://doi.org/10.1007/978-3-319-13972-2_16
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