Automatic brain labeling via multi-atlas guided fully convolutional networks

Longwei Fang, Lichi Zhang, Dong Nie, Xiaohuan Cao, Islem Rekik, Seong Whan Lee, Huiguang He, Dinggang Shen

Research output: Contribution to journalArticlepeer-review

27 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)157-168
Number of pages12
JournalMedical Image Analysis
Publication statusPublished - 2019 Jan

Bibliographical note

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.


  • Brain image labeling
  • Fully convolutional network
  • Multi-atlas-based method
  • Patch-based labeling

ASJC Scopus subject areas

  • Radiological and Ultrasound Technology
  • Radiology Nuclear Medicine and imaging
  • Computer Vision and Pattern Recognition
  • Health Informatics
  • Computer Graphics and Computer-Aided Design


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