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

Mingxia Liu; Chunfeng Lian; Dinggang Shen

doi:10.1007/978-3-030-32606-7_8

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 proceeding › Chapter

9 Citations (Scopus)

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 language	English
Title of host publication	Intelligent Systems Reference Library
Publisher	Springer
Pages	127-147
Number of pages	21
DOIs	https://doi.org/10.1007/978-3-030-32606-7_8
Publication status	Published - 2020
Externally published	Yes

Publication series

Name	Intelligent Systems Reference Library
Volume	171
ISSN (Print)	1868-4394
ISSN (Electronic)	1868-4408

Bibliographical note

Funding Information:
This study was partly supported by NIH grants (EB006733, EB008374, EB009634, MH100217, AG041721, AG042599, AG010129, and AG030514). Data used in preparation of this article were obtained from the Alzheimer?s Disease Neuroimaging Initiative (ADNI) database. The investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report, with details shown online.

Publisher Copyright:
© 2020, Springer Nature Switzerland AG.

ASJC Scopus subject areas

General Computer Science
Information Systems and Management
Library and Information Sciences

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10.1007/978-3-030-32606-7_8

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title = "Anatomical-Landmark-Based Deep Learning for Alzheimer{\textquoteright}s Disease Diagnosis with Structural Magnetic Resonance Imaging",

abstract = "Structural magnetic resonance imaging (sMRI) has been widely used in computer-aided diagnosis of brain diseases, such as Alzheimer{\textquoteright}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.",

author = "Mingxia Liu and Chunfeng Lian and Dinggang Shen",

note = "Funding Information: This study was partly supported by NIH grants (EB006733, EB008374, EB009634, MH100217, AG041721, AG042599, AG010129, and AG030514). Data used in preparation of this article were obtained from the Alzheimer?s Disease Neuroimaging Initiative (ADNI) database. The investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report, with details shown online. Publisher Copyright: {\textcopyright} 2020, Springer Nature Switzerland AG.",

year = "2020",

doi = "10.1007/978-3-030-32606-7_8",

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AU - Lian, Chunfeng

AU - Shen, Dinggang

N1 - Funding Information: This study was partly supported by NIH grants (EB006733, EB008374, EB009634, MH100217, AG041721, AG042599, AG010129, and AG030514). Data used in preparation of this article were obtained from the Alzheimer?s Disease Neuroimaging Initiative (ADNI) database. The investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report, with details shown online. Publisher Copyright: © 2020, Springer Nature Switzerland AG.

PY - 2020

Y1 - 2020

N2 - 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.

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

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