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

Kamnitsas K, Ledig C, Newcombe VFJ, Sirnpson JP, Kane AD, Menon DK, Rueckert D, Glocker B (2017)

Publication Type: Journal article

Publication year: 2017

Journal

Medical Image Analysis Elsevier

Book Volume: 36

Pages Range: 61-78

DOI: 10.1016/j.media.2016.10.004

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 tumours, and ischemic stroke. We improve on the state-of-the-art 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.

Involved external institutions

Imperial College London / The Imperial College of Science, Technology and Medicine GB United Kingdom (GB) University of Cambridge GB United Kingdom (GB)

How to cite

APA:

Kamnitsas, K., Ledig, C., Newcombe, V.F.J., Sirnpson, J.P., Kane, A.D., Menon, D.K.,... Glocker, B. (2017). Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation. Medical Image Analysis, 36, 61-78. https://doi.org/10.1016/j.media.2016.10.004

MLA:

Kamnitsas, Konstantinos, et al. "Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation." Medical Image Analysis 36 (2017): 61-78.

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