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Scalable multimodal convolutional networks for brain tumour segmentation

Fidon, L; Li, W; Garcia-Peraza-Herrera, LC; Ekanayake, J; Kitchen, N; Ourselin, S; Vercauteren, T; (2017) Scalable multimodal convolutional networks for brain tumour segmentation. In: Descoteaux, M and Maier-Hein, L and Franz, A and Jannin, P and Collins, D and Duchesne, S, (eds.) Medical Image Computing and Computer-Assisted Intervention − MICCAI 2017. (pp. pp. 285-293). Springer: Cham. Green open access

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Abstract

Brain tumour segmentation plays a key role in computer-assisted surgery. Deep neural networks have increased the accuracy of automatic segmentation significantly, however these models tend to generalise poorly to different imaging modalities than those for which they have been designed, thereby limiting their applications. For example, a network architecture initially designed for brain parcellation of monomodal T1 MRI can not be easily translated into an efficient tumour segmentation network that jointly utilises T1, T1c, Flair and T2 MRI. To tackle this, we propose a novel scalable multimodal deep learning architecture using new nested structures that explicitly leverage deep features within or across modalities. This aims at making the early layers of the architecture structured and sparse so that the final architecture becomes scalable to the number of modalities. We evaluate the scalable architecture for brain tumour segmentation and give evidence of its regularisation effect compared to the conventional concatenation approach.

Type: Proceedings paper
Title: Scalable multimodal convolutional networks for brain tumour segmentation
Event: Medical Image Computing and Computer-Assisted Intervention − MICCAI 2017
ISBN-13: 9783319661780
Open access status: An open access version is available from UCL Discovery
DOI: 10.1007/978-3-319-66179-7_33
Publisher version: https://doi.org/10.1007/978-3-319-66179-7_33
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
UCL classification: UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > Div of Psychology and Lang Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > Div of Psychology and Lang Sciences > Institute of Cognitive Neuroscience
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Med Phys and Biomedical Eng
URI: http://discovery.ucl.ac.uk/id/eprint/1559898
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