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On the compactness, efficiency, and representation of 3D convolutional networks: Brain parcellation as a pretext task

Li, W; Wang, G; Fidon, L; Ourselin, S; Cardoso, MJ; Vercauteren, T; (2017) On the compactness, efficiency, and representation of 3D convolutional networks: Brain parcellation as a pretext task. In: Niethammer, M and Styner, M and Aylward, S and Zhu, H and Oguz, I and Yap, PT and Shen, D, (eds.) Information Processing in Medical Imaging: 25th International Conference, IPMI 2017, Boone, NC, USA, June 25-30, 2017, Proceedings. (pp. pp. 348-360). Springer: Cham, Switzerland. Green open access

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Abstract

Deep convolutional neural networks are powerful tools for learning visual representations from images. However, designing efficient deep architectures to analyse volumetric medical images remains challenging. This work investigates efficient and flexible elements of modern convolutional networks such as dilated convolution and residual connection. With these essential building blocks, we propose a high-resolution, compact convolutional network for volumetric image segmentation. To illustrate its efficiency of learning 3D representation from large-scale image data, the proposed network is validated with the challenging task of parcellating 155 neuroanatomical structures from brain MR images. Our experiments show that the proposed network architecture compares favourably with state-of-the-art volumetric segmentation networks while being an order of magnitude more compact. We consider the brain parcellation task as a pretext task for volumetric image segmentation; our trained network potentially provides a good starting point for transfer learning. Additionally, we show the feasibility of voxel-level uncertainty estimation using a sampling approximation through dropout.

Type: Proceedings paper
Title: On the compactness, efficiency, and representation of 3D convolutional networks: Brain parcellation as a pretext task
Event: IPMI: International Conference on Information Processing in Medical Imaging
ISBN-13: 9783319590493
Open access status: An open access version is available from UCL Discovery
DOI: 10.1007/978-3-319-59050-9_28
Publisher version: http://dx.doi.org/10.1007/978-3-319-59050-9_28
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
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: https://discovery.ucl.ac.uk/id/eprint/1559899
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