Anatomical-Landmark-Based Deep Learning for Alzheimer’s Disease Diagnosis with Structural Magnetic Resonance Imaging

Mingxia Liu, Chunfeng Lian, Dinggang Shen

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

Structural magnetic resonance imaging (sMRI) has been widely used in computer-aided diagnosis of brain diseases, such as Alzheimer’s disease (AD) and its prodromal stage, i.e., mild cognitive impairment (MCI). Based on sMRI data, anatomical-landmark-based deep learning has been recently proposed for AD and MCI diagnosis. These methods usually first locate informative anatomical landmarks in brain sMR images, and then integrate both feature learning and classification training into a unified framework. This chapter presents the latest anatomical-landmark-based deep learning approaches for automatic diagnosis of AD and MCI. Specifically, an automatic landmark discovery method is first introduced to identify discriminative regions in brain sMR images. Then, a landmark-based deep learning framework is presented for AD/MCI classification, by jointly performing feature extraction and classifier training. Experimental results on three public databases demonstrate that the proposed framework boosts the disease diagnosis performance, compared with several state-of-the-art sMRI-based methods.

Original languageEnglish
Title of host publicationIntelligent Systems Reference Library
PublisherSpringer
Pages127-147
Number of pages21
DOIs
Publication statusPublished - 2020 Jan 1
Externally publishedYes

Publication series

NameIntelligent Systems Reference Library
Volume171
ISSN (Print)1868-4394
ISSN (Electronic)1868-4408

ASJC Scopus subject areas

  • Computer Science(all)
  • Information Systems and Management
  • Library and Information Sciences

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    Liu, M., Lian, C., & Shen, D. (2020). Anatomical-Landmark-Based Deep Learning for Alzheimer’s Disease Diagnosis with Structural Magnetic Resonance Imaging. In Intelligent Systems Reference Library (pp. 127-147). (Intelligent Systems Reference Library; Vol. 171). Springer. https://doi.org/10.1007/978-3-030-32606-7_8