Development of feature engineering and deep learning techniques applied to the classification of magnetic resonance images in healthy cognitive aging, mild cognitive impairment and Alzheimer's Disease

• Grant number: 18/08826-9
• Support Opportunities: Regular Research Grants
• Start date: October 01, 2018
• End date: March 31, 2021
• Field of knowledge: Physical Sciences and Mathematics - Computer Science
• Principal Investigator: Ricardo José Ferrari
• Grantee: Ricardo José Ferrari
• Host Institution: Centro de Ciências Exatas e de Tecnologia (CCET). Universidade Federal de São Carlos (UFSCAR). São Carlos , SP, Brazil
• City of the host institution: São Carlos
• Associated researchers: Roger Tam

Abstract

With the population aging, dementia has become one of the most relevant global public health problems. Among the different types of dementia, Alzheimer's disease (AD) is the most frequent, accounting for almost 60% of the cases. The World Health Organization estimated the number of people with dementia at 35.6 million in 2010, which is expected to double by 2030 (65.7 million) and again by 2050 (115.4 million). In Brazil, the number of people with dementia is estimated at one million. However, even when patients report symptoms and have apparent cognitive impairments, dementia may not be diagnosed. Up to 75% of patients with dementia and up to 97% of patients with a mild cognitive impairment may not be diagnosed. New proposals for diagnostic criteria for AD and prospects for pre-dementia therapies require the identification of biomarkers that provide an early and accurate diagnosis. Therefore, this project proposes the study of feature engineering and deep learning techniques for use in automatic classification of 3D magnetic resonance imaging in the classes healthy cognitive aging, mild cognitive impairment and Alzheimer's disease. All development will be carried out using public domain images databases, and the final developed techniques will be available for use by researchers from the Department of Medicine of the Federal University of São Carlos (UFSCar). (AU)

Scientific publications

(8)

(The scientific publications listed on this page originate from the Web of Science or SciELO databases. Their authors have cited FAPESP grant or fellowship project numbers awarded to Principal Investigators or Fellowship Recipients, whether or not they are among the authors. This information is collected automatically and retrieved directly from those bibliometric databases.)

SOUZA, BRENO DA SILVEIRA; POLONI, KATIA M.; FERRARI, RICARDO J.. Detector of 3-D salient points based on the dual-tree complex wavelet transform for the positioning of hippocampi meshes in magnetic resonance images. JOURNAL OF NEUROSCIENCE METHODS, v. 341, JUL 15 2020. (18/06049-5, 18/08826-9, 17/24391-0)

DA SILVA, BRUNO C. GREGORIO; FERRARI, RICARDO J.; GERVASI, O; MURGANTE, B; MISRA, S; GARAU, C; BLECIC, I; TANIAR, D; APDUHAN, BO; ROCHA, AMAC; et al. Exploring Deep Convolutional Neural Networks as Feature Extractors for Cell Detection. COMPUTATIONAL SCIENCE AND ITS APPLICATIONS - ICCSA 2020, PT II, v. 12250, p. 13-pg., 2020-01-01. (13/26171-6, 18/08826-9)

POLONI, KATIA M.; FERRARI, RICARDO J.. utomated detection, selection and classification of hippocampal landmark points for the diagnosis of Alzheimer's diseas. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE, v. 214, FEB 2022. (18/08826-9, 18/06049-5)

FREIRE, PAULO G. L.; IDAGAWA, MARCOS HIDEKI; LOBATO DE OLIVEIRA, ENEDINA MARIA; ABDALA, NITAMAR; CARRETE JR, HENRIQUE; FERRARI, RICARDO J.; GERVASI, O; MURGANTE, B; MISRA, S; GARAU, C; et al. Classification of Active Multiple Sclerosis Lesions in MRI Without the Aid of Gadolinium-Based Contrast Using Textural and Enhanced Features from FLAIR Images. COMPUTATIONAL SCIENCE AND ITS APPLICATIONS - ICCSA 2020, PT II, v. 12250, p. 15-pg., 2020-01-01. (18/08826-9, 16/15661-0)

POLONI, KATIA MARIA; FERRARI, RICARDO JOSE; ALZHEIMER'S DIS NEUROIMAGING INITI. A deep ensemble hippocampal CNN model for brain age estimation applied to Alzheimer's diagnosis. EXPERT SYSTEMS WITH APPLICATIONS, v. 195, p. 12-pg., 2022-02-11. (18/06049-5, 18/08826-9)

KORB, MATHEUS MULLER; FERRARI, RICARDO JOSE; ALZHEIMER'S DIS NEUROIMAGING; GERVASI, O; MURGANTE, B; MISRA, S; GARAU, C; BLECIC, I; TANIAR, D; APDUHAN, BO; et al. Automatic Positioning of Hippocampus Deformable Mesh Models in Brain MR Images Using a Weighted 3D-SIFT Technique. COMPUTATIONAL SCIENCE AND ITS APPLICATIONS - ICCSA 2020, PT II, v. 12250, p. 16-pg., 2020-01-01. (18/08826-9)

DE OLIVEIRA, ITALO A. D.; POLONI, KATIA M.; FERRARI, RICARDO J.; DEHERRERA, AGS; GONZALEZ, AR; SANTOSH, KC; TEMESGEN, Z; KANE, B; SODA, P. Exploring hippocampal asymmetrical features from magnetic resonance images for the classification of Alzheimer's disease. 2020 IEEE 33RD INTERNATIONAL SYMPOSIUM ON COMPUTER-BASED MEDICAL SYSTEMS(CBMS 2020), v. N/A, p. 6-pg., 2020-01-01. (18/09972-9, 18/08826-9, 18/06049-5)

POLONI, KATIA M.; DE OLIVEIRA, ITALO A. DUARTE; TAM, ROGER; FERRARI, RICARDO J.; INITI, ALZHEIMERS DIS NEUROIMAGING. Brain MR image classification for Alzheimer's disease diagnosis using structural hippocampal asymmetrical attributes from directional 3-D log-Gabor filter responses. Neurocomputing, v. 419, p. 126-135, JAN 2 2021. (18/09972-9, 18/06049-5, 18/08826-9)