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 journalArticlepeer-review

48 Citations (Scopus)

Abstract

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 defined by experts. Also, because of 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. We first identify discriminative anatomical landmarks from MR images in a data-driven manner, and then propose a convolutional neural network 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 disease classification and MR image retrieval.

Original languageEnglish
Article number8253440
Pages (from-to)1476-1485
Number of pages10
JournalIEEE Journal of Biomedical and Health Informatics
Volume22
Issue number5
DOIs
Publication statusPublished - 2018 Sep

Keywords

  • Anatomical landmarks
  • brain disease diagnosis
  • classification
  • convolutional neural network
  • image retrieval

ASJC Scopus subject areas

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

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