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Automatic brain labeling via multi-atlas guided fully convolutional networks
Longwei Fang1,2,5; Lichi Zhang4,5; Dong Nie5; Xiaohuan Cao5,7; Islem Rekik8; Seong-Whan Lee6; Huiguang He1,2,3; Dingguang Shen5,6
Source PublicationMedical Image Analysis

Multi-atlas-based methods are commonly used for MR brain image labeling, which alleviates the burdening and time-consuming task of manual labeling in neuroimaging analysis studies. Traditionally, multi-atlas-based methods first register multiple atlases to the target image, and then propagate the labels from the labeled atlases to the unlabeled target image. However, the registration step involves non-rigid alignment, which is often time-consuming and might lack high accuracy. Alternatively, patch-based methods have shown promise in relaxing the demand for accurate registration, but they often require the use of hand-crafted features. Recently, deep learning techniques have demonstrated their effectiveness in image labeling, by automatically learning comprehensive appearance features from training images. In this paper, we propose a multi-atlas guided fully convolutional network (MA-FCN) for automatic image labeling, which aims at further improving the labeling performance with the aid of prior knowledge from the training atlases. Specifically, we train our MA-FCN model in a patch-based manner, where the input data consists of not only a training image patch but also a set of its neighboring (i.e., most similar) affine-aligned atlas patches. The guidance information from neighboring atlas patches can help boost the discriminative ability of the learned FCN. Experimental results on different datasets demonstrate the effectiveness of our proposed method, by significantly outperforming the conventional FCN and several state-of-the-art MR brain labeling methods.

KeywordBrain Image Labeling, Multi-atlas-based Method, Fully Convolutional Network, Patch-based Labeling
Indexed BySCI
WOS IDWOS:000454368400011
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Cited Times:12[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Corresponding AuthorHuiguang He; Dingguang Shen
Affiliation1.Instituteof Automation, Chinese Academy of Sciences, Beijing, China
2.University of Chinese Academy of Sciences, Beijing, China
3.Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Scienc-es, Beijing, China
4.Institute for Medical Imaging Technology, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
5.Department of Radiology and BRIC, University of North Carolina at Chapel Hill, North Carolina, USA
6.Department of Brain and Cognitive Engineering, Korea University, Seoul, Korea
7.School of Automation, Northwestern Polytechnical University, Xi’an, China
8.BASIRA lab, CVIP, School of Science and Engineering, Computing, University of Dundee, UK
Recommended Citation
GB/T 7714
Longwei Fang,Lichi Zhang,Dong Nie,et al. Automatic brain labeling via multi-atlas guided fully convolutional networks[J]. Medical Image Analysis,2019(52):157-168.
APA Longwei Fang.,Lichi Zhang.,Dong Nie.,Xiaohuan Cao.,Islem Rekik.,...&Dingguang Shen.(2019).Automatic brain labeling via multi-atlas guided fully convolutional networks.Medical Image Analysis(52),157-168.
MLA Longwei Fang,et al."Automatic brain labeling via multi-atlas guided fully convolutional networks".Medical Image Analysis .52(2019):157-168.
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