DeepMRS: An End-to-End Deep Neural Network for Dementia Disease Detection using MRS Data
| Autor: | Christine Fernandez-Maloigne, Carole Guillevin, Benoit Gianelli, Lobna Fezai, Seifeddine Fezzani, Olfa Ben Ahmed, Mathieu Naudin |
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| Přispěvatelé: | Synthèse et analyse d'images (XLIM-ASALI), XLIM (XLIM), Université de Limoges (UNILIM)-Centre National de la Recherche Scientifique (CNRS)-Université de Limoges (UNILIM)-Centre National de la Recherche Scientifique (CNRS), Université de Poitiers, Laboratoire de Mathématiques et Applications (LMA-Poitiers), Université de Poitiers-Centre National de la Recherche Scientifique (CNRS), Centre hospitalier universitaire de Poitiers (CHU Poitiers) |
| Rok vydání: | 2020 |
| Předmět: |
MRS
medicine.medical_specialty Disease [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI] 030218 nuclear medicine & medical imaging 03 medical and health sciences 0302 clinical medicine Physical medicine and rehabilitation Atrophy Neuroimaging [INFO.INFO-IM]Computer Science [cs]/Medical Imaging medicine Dementia Artificial neural network business.industry Deep learning deep learning [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV] medicine.disease Computer-aided diagnosis Posterior cingulate computer-aided diagnosis Artificial intelligence [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM] business Alzheimer’s disease 030217 neurology & neurosurgery |
| Zdroj: | ISBI IEEE International Symposium on Biomedical Imaging (ISBI) 2020 IEEE International Symposium on Biomedical Imaging (ISBI) 2020, Apr 2020, Iowa City, Iowa, United States |
| DOI: | 10.1109/isbi45749.2020.9098419 |
| Popis: | Alzheimer‘s disease (AD) is the most common form of dementia. Neuroimaging data is an integral part of the clinical assessment providing a way for clinicians to detect brain abnormalities for AD diagnosis. Anatomical MRI has been widely used to assess structural brain atrophy for AD detection and prediction. In addition to structural changes, metabolic changes in some brain regions such as the Posterior Cingulate Cortex (PCC) could be a good bio-marker for an early AD detection. Recently, proton Magnetic Resonance Spectroscopy (1H-MRS) have been proved to be effective to reveal a wealth of brain metabolic information. In this paper, we propose an end-to-end deep leaning Network for early AD and Normal Control (NC) subjects classification using 1H-MRS raw data from the PCC area. This work is the first investigation of 1H - MRS data with deep-learning technique for early AD detection. Data of 135 subjects, collected in Poitiers university hospital, are used to learn the proposed DeepMRS network. Our classification of patients with early AD versus NC subjects achieves an AUC of 94,74%, a sensitivity of 100% and a specificity of 89,47% demonstrating a promising early dementia detection performance. Alzheimer's disease, deep learning, MRS, computer-aided diagnosis |
| Databáze: | OpenAIRE |
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