Anatomical Landmark based Deep Feature Representation for MR Images in Brain Disease Diagnosis

Mingxia Liu, Jun Zhang, Dong Nie, Pew Thian Yap, Dinggang Shen

Research output: Contribution to journalArticle

30 Citations (Scopus)


Most automated techniques for brain disease diagnosis utilize hand-crafted (e.g., voxel-based or region-based) biomarkers from structural magnetic resonance (MR) images as feature representations. However, these hand-crafted features are usually high-dimensional or require regions-of-interest (ROIs) defined by experts. In addition, because of the possibly heterogeneous property between the hand-crafted features and the subsequent model, existing methods may lead to sub-optimal performances in brain disease diagnosis. In this paper, we propose a landmark based deep feature learning (LDFL) framework to automatically extract patch-based representation from MRI for automatic diagnosis of Alzheimer's disease (AD). We first identify discriminative anatomical landmarks from MR images in a data-driven manner, and then propose a convolutional neural network (CNN) for patch-based deep feature learning. We have evaluated the proposed method on subjects from three public datasets, including the Alzheimer's Disease Neuroimaging Initiative (ADNI-1), ADNI-2, and the Minimal Interval Resonance Imaging in Alzheimer's Disease (MIRIAD) dataset. Experimental results of both tasks of brain disease classification and MR image retrieval demonstrate that the proposed LDFL method improves the performance of brain disease classification and MR image retrieval.

Original languageEnglish
JournalIEEE Journal of Biomedical and Health Informatics
Publication statusAccepted/In press - 2018 Jan 9


  • Anatomical landmarks
  • brain disease diagnosis
  • classification
  • convolutional neural network
  • Dementia
  • Feature extraction
  • Image retrieval
  • image retrieval
  • Testing
  • Training

ASJC Scopus subject areas

  • Biotechnology
  • Computer Science Applications
  • Electrical and Electronic Engineering
  • Health Information Management

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