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Contour Propagation in CT Scans with Convolutional Neural Networks

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Léger, Jean [UCL] Brion, Eliott [UCL] Javaid, Umair [UCL] Lee, John Aldo [UCL] De Vleeschouwer, Christophe [UCL] Macq, Benoît [UCL]

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.

Document type Communication à un colloque (Conference Paper) – Présentation orale avec comité de sélection
Access type Accès libre
Publication date 2018
Language Anglais
Conference "Advanced Concepts for Intelligent Vision Systems", France (du 24/09/2018 au 27/09/2018)
Peer reviewed yes
Host document "Proceedings of ACIVS, in Lecture Notes in Computer Science book series." (ISBN : 978-3-030-01449-0)
Publication status Publié
Affiliation UCL - SST/ICTM/ELEN - Pôle en ingénierie électrique
Links
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Bibliographic reference Léger, Jean ; Brion, Eliott ; Javaid, Umair ; Lee, John Aldo ; De Vleeschouwer, Christophe ; et. al. Contour Propagation in CT Scans with Convolutional Neural Networks.Advanced Concepts for Intelligent Vision Systems (France, du 24/09/2018 au 27/09/2018). In: Proceedings of ACIVS, in Lecture Notes in Computer Science book series., 2018
Permanent URL http://hdl.handle.net/2078.1/203221