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

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• 作者：Konstantinos Kamnitsas ; ^a ; ^{konstantinos.kamnitsas12@imperial.ac.uk} ; Christian Ledig^a ; Virginia F.J. Newcombe^b ; ^c ; Joanna P. Simpson^b ; Andrew D. Kane^b ; David K. Menon^b ; ^c ; Daniel Rueckert^a ; Ben Glocker^a
• 关键词：3D convolutional neural network ; Fully connected CRF ; Segmentation ; Brain lesions ; Deep learning
• 刊名：Medical Image Analysis
• 出版年：2017
• 出版时间：February 2017
• 年：2017
• 卷：36
• 期：Complete
• 页码：61-78
• 全文大小：5021 K
• 卷排序：36

文摘

An efficient 11-layers deep, multi-scale, 3D CNN architecture. A novel training strategy that significantly boosts performance. The first employment of a 3D fully connected CRF for post-processing. State-of-the-art performance on three challenging lesion segmentation tasks. New insights into the automatically learned intermediate representations.