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Khan R, Akbar S, Mehmood A, Shahid F, Munir K, Ilyas N, Asif M, Zheng Z. A transfer learning approach for multiclass classification of Alzheimer's disease using MRI images. Front Neurosci 2023; 16:1050777. [PMID: 36699527 PMCID: PMC9869687 DOI: 10.3389/fnins.2022.1050777] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2022] [Accepted: 12/05/2022] [Indexed: 01/11/2023] Open
Abstract
Alzheimer's is an acute degenerative disease affecting the elderly population all over the world. The detection of disease at an early stage in the absence of a large-scale annotated dataset is crucial to the clinical treatment for the prevention and early detection of Alzheimer's disease (AD). In this study, we propose a transfer learning base approach to classify various stages of AD. The proposed model can distinguish between normal control (NC), early mild cognitive impairment (EMCI), late mild cognitive impairment (LMCI), and AD. In this regard, we apply tissue segmentation to extract the gray matter from the MRI scans obtained from the Alzheimer's Disease National Initiative (ADNI) database. We utilize this gray matter to tune the pre-trained VGG architecture while freezing the features of the ImageNet database. It is achieved through the addition of a layer with step-wise freezing of the existing blocks in the network. It not only assists transfer learning but also contributes to learning new features efficiently. Extensive experiments are conducted and results demonstrate the superiority of the proposed approach.
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Affiliation(s)
- Rizwan Khan
- Department of Computer Science and Mathematics, Zhejiang Normal University, Jinhua, China,*Correspondence: Rizwan Khan ✉
| | - Saeed Akbar
- School of Computer Science, Huazhong University of Science and Technology, Wuhan, China
| | - Atif Mehmood
- Division of Biomedical Imaging, Department of Biomedical Engineering and Health Systems, KTH Royal Institute of Technology, Stockholm, Sweden,Department of Computer Science, National University of Modern Languages, Islamabad, Pakistan
| | - Farah Shahid
- Department of Computer Science, University of Agriculture, Sub Campus Burewala-Vehari, Faisalabad, Pakistan
| | - Khushboo Munir
- Department of Radiology and Diagnostic Imaging, University of Alberta, Edmonton, AB, Canada
| | - Naveed Ilyas
- Department of Physics, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates
| | - M. Asif
- Department of Radiology, Emory Brain Health Center-Neurosurgery, School of Medicine, Emory University, Atlanta, GA, United States
| | - Zhonglong Zheng
- Department of Computer Science and Mathematics, Zhejiang Normal University, Jinhua, China,Key Laboratory of Intelligent Education Technology and Application of Zhejiang Province, Zhejiang Normal University, Jinhua, China
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Feng J, Zhang SW, Chen L, Zuo C. Detection of Alzheimer’s Disease Using Features of Brain Region-of-Interest-Based Individual Network Constructed with the sMRI Image. Comput Med Imaging Graph 2022; 98:102057. [DOI: 10.1016/j.compmedimag.2022.102057] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2021] [Revised: 02/18/2022] [Accepted: 03/17/2022] [Indexed: 10/18/2022]
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Neurodegenerative diseases-Caps: a capsule network based early screening system for the classification of neurodegenerative diseases. Cogn Neurodyn 2022; 16:1361-1377. [DOI: 10.1007/s11571-022-09787-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2021] [Revised: 12/16/2021] [Accepted: 01/21/2022] [Indexed: 11/03/2022] Open
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Li R, Wang X, Lawler K, Garg S, Bai Q, Alty J. Applications of Artificial Intelligence to aid detection of dementia: a scoping review on current capabilities and future directions. J Biomed Inform 2022; 127:104030. [DOI: 10.1016/j.jbi.2022.104030] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2021] [Revised: 01/21/2022] [Accepted: 02/12/2022] [Indexed: 12/17/2022]
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Feng J, Zhang SW, Chen L, Xia J. Alzheimer’s disease classification using features extracted from nonsubsampled contourlet subband-based individual networks. Neurocomputing 2021. [DOI: 10.1016/j.neucom.2020.09.012] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
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Mendoza-Léon R, Puentes J, Uriza LF, Hernández Hoyos M. Single-slice Alzheimer's disease classification and disease regional analysis with Supervised Switching Autoencoders. Comput Biol Med 2019; 116:103527. [PMID: 31765915 DOI: 10.1016/j.compbiomed.2019.103527] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2019] [Revised: 10/23/2019] [Accepted: 10/28/2019] [Indexed: 12/11/2022]
Abstract
BACKGROUND Alzheimer's disease (AD) is a difficult to diagnose pathology of the brain that progressively impairs cognitive functions. Computer-assisted diagnosis of AD based on image analysis is an emerging tool to support AD diagnosis. In this article, we explore the application of Supervised Switching Autoencoders (SSAs) to perform AD classification using only one structural Magnetic Resonance Imaging (sMRI) slice. SSAs are revised supervised autoencoder architectures, combining unsupervised representation and supervised classification as one unified model. In this work, we study the capabilities of SSAs to capture complex visual neurodegeneration patterns, and fuse disease semantics simultaneously. We also examine how regions associated to disease state can be discovered by SSAs following a local patch-based approach. METHODS Patch-based SSAs models are trained on individual patches extracted from a single 2D slice, independently for Axial, Coronal, and Sagittal anatomical planes of the brain at selected informative locations, exploring different patch sizes and network parameterizations. Then, models perform binary class prediction - healthy (CDR = 0) or AD-demented (CDR > 0) - on test data at patch level. The final subject classification is performed employing a majority rule from the ensemble of patch predictions. In addition, relevant regions are identified, by computing accuracy densities from patch-level predictions, and analyzed, supported by Atlas-based regional definitions. RESULTS Our experiments employing a single 2D T1-w sMRI slice per subject show that SSAs perform similarly to previous proposals that rely on full volumetric information and feature-engineered representations. SSAs classification accuracy on slices extracted along the Axial, Coronal, and Sagittal anatomical planes from a balanced cohort of 40 independent test subjects was 87.5%, 90.0%, and 90.0%, respectively. A top sensitivity of 95.0% on both Coronal and Sagittal planes was also obtained. CONCLUSIONS SSAs provided well-ranked accuracy performance among previous classification proposals, including feature-engineered and feature learning based methods, using only one scan slice per subject, instead of the whole 3D volume, as it is conventionally done. In addition, regions identified as relevant by SSAs' were, in most part, coherent or partially coherent in regard to relevant regions reported on previous works. These regions were also associated with findings from medical knowledge, which gives value to our methodology as a potential analytical aid for disease understanding.
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Affiliation(s)
- Ricardo Mendoza-Léon
- Systems and Computing Engineering Department, School of Engineering, Universidad de los Andes, Bogotá, Colombia; IMT Atlantique, Lab-STICC, UMR CNRS 6285, F-29238, Brest, France.
| | - John Puentes
- IMT Atlantique, Lab-STICC, UMR CNRS 6285, F-29238, Brest, France
| | - Luis Felipe Uriza
- Departamento de Radiología e Imágenes Diagnósticas, Hospital Universitario de San Ignacio, Bogotá, Colombia; Facultad de Medicina, Pontificia Universidad Javeriana, Bogotá, Colombia
| | - Marcela Hernández Hoyos
- Systems and Computing Engineering Department, School of Engineering, Universidad de los Andes, Bogotá, Colombia
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Avesani P, McPherson B, Hayashi S, Caiafa CF, Henschel R, Garyfallidis E, Kitchell L, Bullock D, Patterson A, Olivetti E, Sporns O, Saykin AJ, Wang L, Dinov I, Hancock D, Caron B, Qian Y, Pestilli F. The open diffusion data derivatives, brain data upcycling via integrated publishing of derivatives and reproducible open cloud services. Sci Data 2019; 6:69. [PMID: 31123325 PMCID: PMC6533280 DOI: 10.1038/s41597-019-0073-y] [Citation(s) in RCA: 45] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2018] [Accepted: 04/11/2019] [Indexed: 12/31/2022] Open
Abstract
We describe the Open Diffusion Data Derivatives (O3D) repository: an integrated collection of preserved brain data derivatives and processing pipelines, published together using a single digital-object-identifier. The data derivatives were generated using modern diffusion-weighted magnetic resonance imaging data (dMRI) with diverse properties of resolution and signal-to-noise ratio. In addition to the data, we publish all processing pipelines (also referred to as open cloud services). The pipelines utilize modern methods for neuroimaging data processing (diffusion-signal modelling, fiber tracking, tractography evaluation, white matter segmentation, and structural connectome construction). The O3D open services can allow cognitive and clinical neuroscientists to run the connectome mapping algorithms on new, user-uploaded, data. Open source code implementing all O3D services is also provided to allow computational and computer scientists to reuse and extend the processing methods. Publishing both data-derivatives and integrated processing pipeline promotes practices for scientific reproducibility and data upcycling by providing open access to the research assets for utilization by multiple scientific communities.
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Affiliation(s)
- Paolo Avesani
- Neuroinformatics Laboratory, Center for Information Technology, Fondazione Bruno Kessler, via Sommarive 18, 38123, Trento, Italy
- Center for Mind/Brain Sciences (CIMeC), University of Trento, via Delle Regole 101, 38123, Trento, Italy
| | - Brent McPherson
- Pestilli Lab. Department of Psychological and Brain Sciences, Program in Cognitive Science, Indiana University Bloomington, 1101 E 10th Street, Bloomington, Indiana, 47405, USA
| | - Soichi Hayashi
- Department of Psychological and Brain Sciences and Pervasive Technology Institute, University Information Technology Services, Indiana University, 1101 E 10th Street, Bloomington, IN, 47405, USA
| | - Cesar F Caiafa
- Pestilli Lab. Department of Psychological and Brain Sciences, Indiana University Bloomington, 1101 E 10th Street, Bloomington, Indiana, 47405, USA
- Instituto Argentino de Radioastronomía (CCT-La Plata, CONICET; CICPBA), CC5 V, Elisa, 1894, Argentina
- Facultad de Ingeniería, Universidad de Buenos Aires, Buenos Aires, C1063ACV, Argentina
| | - Robert Henschel
- Pervasive Technology Institute, Indiana University Bloomington, 2709 E 10th Street, Bloomington, IN, 47408, USA
| | - Eleftherios Garyfallidis
- Department of Intelligent Systems Engineering, Programs in Neuroscience and Cognitive Science, Indiana University Bloomington, 700N Woodlawn Ave, Bloomington, Indiana, 47408, USA
| | - Lindsey Kitchell
- Pestilli Lab. Department of Psychological and Brain Sciences, Program in Cognitive Science, Indiana University Bloomington, 1101 E 10th Street, Bloomington, Indiana, 47405, USA
| | - Daniel Bullock
- Pestilli Lab. Department of Psychological and Brain Sciences, Program in Neuroscience, Indiana University Bloomington, 1101 E 10th Street, Bloomington, Indiana, 47405, USA
| | - Andrew Patterson
- Pestilli Lab. Department of Psychological and Brain Sciences, Program in Neuroscience, Indiana University Bloomington, 1101 E 10th Street, Bloomington, Indiana, 47405, USA
| | - Emanuele Olivetti
- Neuroinformatics Laboratory, Center for Information Technology, Fondazione Bruno Kessler, via Sommarive 18, 38123, Trento, Italy
- Center for Mind/Brain Sciences (CIMeC), University of Trento, via Delle Regole 101, 38123, Trento, Italy
| | - Olaf Sporns
- Department of Psychological and Brain Sciences, Programs in Neuroscience and Cognitive Science, and Indiana Network Science Institute, Indiana University Bloomington, 1101 E 10th Street, Bloomington, Indiana, 47405, USA
| | - Andrew J Saykin
- Indiana University School of Medicine, Departments of Radiology and Imaging Sciences and Medical and Molecular Genetics, and the Indiana Alzheimer Disease Center, Indiana University, 355 W 16th St., Indianapolis, Indiana, 46202, USA
| | - Lei Wang
- Departments of Psychiatry and Behavioral Sciences and Radiology, Northwestern University Feinberg School of Medicine, 710N. Lake Shore Drive, Abbott Hall 1322, Chicago, IL, 60611, USA
| | - Ivo Dinov
- Statistics Online Computational Resource (SOCR), Center for Complexity of Self-Management in Chronic Disease (CSCD), Health Behavior and Biological Sciences, Michigan Institute for Data Science (MIDAS), University of Michigan, Ann Arbor, MI, 49109, USA
| | - David Hancock
- Pervasive Technology Institute, Indiana University Bloomington, 2709 E 10th Street, Bloomington, IN, 47408, USA
| | - Bradley Caron
- Pestilli Lab. Indiana University School of Optometry and Program in Neuroscience, Indiana University Bloomington, 1101 E 10th Street, Bloomington, Indiana, USA
| | - Yiming Qian
- Pestilli Lab. Department of Psychological and Brain Sciences, Indiana University Bloomington, 1101 E 10th Street, Bloomington, Indiana, 47405, USA
| | - Franco Pestilli
- Pestilli Lab. Department of Psychological and Brain Sciences, Engineering, Computer Science, Programs in Neuroscience and Cognitive Science, School of Optometry, and Indiana Network Science Institute, Indiana University Bloomington, 1101 E 10th Street, Bloomington, Indiana, 47405, USA.
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Sakai K, Yamada K. Machine learning studies on major brain diseases: 5-year trends of 2014–2018. Jpn J Radiol 2018; 37:34-72. [DOI: 10.1007/s11604-018-0794-4] [Citation(s) in RCA: 73] [Impact Index Per Article: 12.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2018] [Accepted: 11/14/2018] [Indexed: 12/17/2022]
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Multi-class Alzheimer's disease classification using image and clinical features. Biomed Signal Process Control 2018. [DOI: 10.1016/j.bspc.2018.02.019] [Citation(s) in RCA: 66] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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Glozman T, Bruckert L, Pestilli F, Yecies DW, Guibas LJ, Yeom KW. Framework for shape analysis of white matter fiber bundles. Neuroimage 2018; 167:466-477. [PMID: 29203454 PMCID: PMC5845796 DOI: 10.1016/j.neuroimage.2017.11.052] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2017] [Revised: 10/26/2017] [Accepted: 11/22/2017] [Indexed: 12/24/2022] Open
Abstract
Diffusion imaging coupled with tractography algorithms allows researchers to image human white matter fiber bundles in-vivo. These bundles are three-dimensional structures with shapes that change over time during the course of development as well as in pathologic states. While most studies on white matter variability focus on analysis of tissue properties estimated from the diffusion data, e.g. fractional anisotropy, the shape variability of white matter fiber bundle is much less explored. In this paper, we present a set of tools for shape analysis of white matter fiber bundles, namely: (1) a concise geometric model of bundle shapes; (2) a method for bundle registration between subjects; (3) a method for deformation estimation. Our framework is useful for analysis of shape variability in white matter fiber bundles. We demonstrate our framework by applying our methods on two datasets: one consisting of data for 6 normal adults and another consisting of data for 38 normal children of age 11 days to 8.5 years. We suggest a robust and reproducible method to measure changes in the shape of white matter fiber bundles. We demonstrate how this method can be used to create a model to assess age-dependent changes in the shape of specific fiber bundles. We derive such models for an ensemble of white matter fiber bundles on our pediatric dataset and show that our results agree with normative human head and brain growth data. Creating these models for a large pediatric longitudinal dataset may improve understanding of both normal development and pathologic states and propose novel parameters for the examination of the pediatric brain.
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Affiliation(s)
- Tanya Glozman
- Electrical Engineering, Stanford University, Stanford, CA, USA.
| | | | - Franco Pestilli
- Psychological and Brain Sciences, Indiana University, Bloomington, IN, USA
| | - Derek W Yecies
- Pediatric Neurosurgery, Stanford University, Stanford, CA, USA
| | | | - Kristen W Yeom
- Department of Radiology, Lucile Packard Children's Hospital, Stanford University, Stanford, CA, USA
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