Abstract
Although deep convolutional neural networks (CNNs) have outperformed state-of-the-art in many medical image segmentation tasks, deep network architectures generally fail in exploiting common sense prior to drive the segmentation. In particular, the availability of a segmented (source) image observed in a CT slice that is adjacent to the slice to be segmented (or target image) has not been considered to improve the deep models segmentation accuracy. In this paper, we investigate a CNN architecture that maps a joint input, composed of the target image and the source segmentation, to a target segmentation. We observe that our solution succeeds in taking advantage of the source segmentation when it is sufficiently close to the target segmentation, without being penalized when the source is far from the target.
J. Léger and E. Brion—Contributed equally.
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Acknowledgments
Jean Léger is a Research Fellow of the Fonds de la Recherche Scientifique - FNRS, Eliott Brion’s work was supported by FEDER-RW project UserMEDIA, Umair Javaid is a Research Fellow funded by the FNRS Televie grant no. 7.4625.16, John A. Lee and Christophe De Vleeschouwer are Senior Research Associates with the Belgian F.R.S.-FNRS. We thank CHU-UCL-Namur (Dr J.-F. Daisne) as well as CHU-Charleroi (Dr N. Meert) for providing the data.
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Léger, J., Brion, E., Javaid, U., Lee, J., De Vleeschouwer, C., Macq, B. (2018). Contour Propagation in CT Scans with Convolutional Neural Networks. In: Blanc-Talon, J., Helbert, D., Philips, W., Popescu, D., Scheunders, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2018. Lecture Notes in Computer Science(), vol 11182. Springer, Cham. https://doi.org/10.1007/978-3-030-01449-0_32
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