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2018 | OriginalPaper | Buchkapitel

Contour Propagation in CT Scans with Convolutional Neural Networks

verfasst von : Jean Léger, Eliott Brion, Umair Javaid, John Lee, Christophe De Vleeschouwer, Benoit Macq

Erschienen in: Advanced Concepts for Intelligent Vision Systems

Verlag: Springer International Publishing

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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.

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Vorheriges Kapitel Optimum Network/Framework Selection from High-Level Specifications in Embedded Deep Learning Vision Applications

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Titel: Contour Propagation in CT Scans with Convolutional Neural Networks
verfasst von: Jean Léger
Eliott Brion
Umair Javaid
John Lee
Christophe De Vleeschouwer
Benoit Macq
Verlag: Springer International Publishing
Buch: Advanced Concepts for Intelligent Vision Systems
Print ISBN: 978-3-030-01448-3

Electronic ISBN: 978-3-030-01449-0

Copyright-Jahr: 2018
DOI: https://doi.org/10.1007/978-3-030-01449-0_32

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