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Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation

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Title: Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation
Authors: Kamnitsas, K
Ledig, C
Newcombe, VFJ
Simpson, JP
Kane, AD
Menon, DK
Rueckert, D
Glocker, B
Item Type: Journal Article
Abstract: We propose a dual pathway, 11-layers deep, three-dimensional Convolutional Neural Network for the challenging task of brain lesion segmentation. The devised architecture is the result of an in-depth analysis of the limitations of current networks proposed for similar applications. To overcome the computational burden of processing 3D medical scans, we have devised an efficient and effective dense training scheme which joins the processing of adjacent image patches into one pass through the network while automatically adapting to the inherent class imbalance present in the data. Further, we analyze the development of deeper, thus more discriminative 3D CNNs. In order to incorporate both local and larger contextual information, we employ a dual pathway architecture that processes the input images at multiple scales simultaneously. For post-processing of the network’s soft segmentation, we use a 3D fully connected Conditional Random Field which effectively removes false positives. Our pipeline is extensively evaluated on three challenging tasks of lesion segmentation in multi-channel MRI patient data with traumatic brain injuries, brain tumors, and ischemic stroke. We improve on the state-of-theart for all three applications, with top ranking performance on the public benchmarks BRATS 2015 and ISLES 2015. Our method is computationally efficient, which allows its adoption in a variety of research and clinical settings. The source code of our implementation is made publicly available
Issue Date: 29-Oct-2016
Date of Acceptance: 12-Oct-2016
URI: http://hdl.handle.net/10044/1/53839
DOI: https://dx.doi.org/10.1016/j.media.2016.10.004
ISSN: 1361-8423
Publisher: Elsevier
Start Page: 61
End Page: 78
Journal / Book Title: Medical Image Analysis
Volume: 36
Copyright Statement: © 2016 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license ( http://creativecommons.org/licenses/by/4.0/ ).
Sponsor/Funder: Commission of the European Communities
Engineering & Physical Science Research Council (EPSRC)
Funder's Grant Number: HEALTH-F2-2013-602150
EP/N023668/1
Keywords: Science & Technology
Technology
Life Sciences & Biomedicine
Computer Science, Artificial Intelligence
Computer Science, Interdisciplinary Applications
Engineering, Biomedical
Radiology, Nuclear Medicine & Medical Imaging
Computer Science
Engineering
3D convolutional neural network
Fully connected CRF
Segmentation Brain lesions
Deep learning
CONVOLUTIONAL NEURAL-NETWORK
TRAUMATIC AXONAL INJURY
HIGH-GRADE GLIOMAS
MULTIPLE-SCLEROSIS
DEFORMABLE REGISTRATION
OUTCOME PREDICTION
TUMOR SEGMENTATION
ISCHEMIC-STROKE
MR-IMAGES
MODEL
Brain lesions
Segmentation
cs.CV
cs.AI
09 Engineering
11 Medical And Health Sciences
Nuclear Medicine & Medical Imaging
Publication Status: Published
Appears in Collections:Faculty of Engineering
Computing



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