TY - JOUR
T1 - Automatic brain labeling via multi-atlas guided fully convolutional networks
AU - Fang, Longwei
AU - Zhang, Lichi
AU - Nie, Dong
AU - Cao, Xiaohuan
AU - Rekik, Islem
AU - Lee, Seong Whan
AU - He, Huiguang
AU - Shen, Dinggang
N1 - Funding Information:
This work was supported in part by The National Key Research and Development Program of China (2017YFB1302704) and National Natural Science Foundation of China ( 91520202, 81701785 ), Youth Innovation Promotion Association CAS ( 2012124 ), the CAS Scientific Research Equipment Development Project (YJKYYQ20170050) and the Beijing Municipal Science & Technology Commission (Z181100008918010) and Strategic Priority Research Program of CAS. This work was also supported by NIH grants ( EB006733, EB008374, MH100217, MH108914, AG041721, AG049371, AG042599, AG053867, EB022880, MH110274 ). Dr. S.-W. Lee was partially supported by Institute for Information & Communications Technology Promotion (IITP) grant funded by the Korea government (No. 2017-0-00451).
Publisher Copyright:
© 2018 Elsevier B.V.
PY - 2019/1
Y1 - 2019/1
N2 - 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.
AB - 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.
KW - Brain image labeling
KW - Fully convolutional network
KW - Multi-atlas-based method
KW - Patch-based labeling
UR - http://www.scopus.com/inward/record.url?scp=85056564930&partnerID=8YFLogxK
U2 - 10.1016/j.media.2018.10.012
DO - 10.1016/j.media.2018.10.012
M3 - Article
C2 - 30447544
AN - SCOPUS:85056564930
SN - 1361-8415
VL - 51
SP - 157
EP - 168
JO - Medical Image Analysis
JF - Medical Image Analysis
ER -