Hippocampus segmentation in magnetic resonance images of Alzheimer's patients using Deep machine learning Article Swipe
Hadi Varmazyar
,
Hossein Yousefi-Banaem
,
Saber Malekzadeh
,
Nahideh Gharehaghaji
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2106.06743
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2106.06743
Background: Alzheimers disease is a progressive neurodegenerative disorder and the main cause of dementia in aging. Hippocampus is prone to changes in the early stages of Alzheimers disease. Detection and observation of the hippocampus changes using magnetic resonance imaging (MRI) before the onset of Alzheimers disease leads to the faster preventive and therapeutic measures. Objective: The aim of this study was the segmentation of the hippocampus in magnetic resonance (MR) images of Alzheimers patients using deep machine learning method. Methods: U-Net architecture of convolutional neural network was proposed to segment the hippocampus in the real MRI data. The MR images of the 100 and 35 patients available in Alzheimers disease Neuroimaging Initiative (ADNI) dataset, was used for the train and test of the model, respectively. The performance of the proposed method was compared with manual segmentation by measuring the similarity metrics. Results: The desired segmentation achieved after 10 iterations. A Dice similarity coefficient (DSC) = 92.3%, sensitivity = 96.5%, positive predicted value (PPV) = 90.4%, and Intersection over Union (IoU) value for the train 92.94 and test 92.93 sets were obtained which are acceptable. Conclusion: The proposed approach is promising and can be extended in the prognosis of Alzheimers disease by the prediction of the hippocampus volume changes in the early stage of the disease.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2106.06743
- https://arxiv.org/pdf/2106.06743
- OA Status
- green
- Cited By
- 1
- References
- 27
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3171181979
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Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3171181979Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2106.06743Digital Object Identifier
- Title
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Hippocampus segmentation in magnetic resonance images of Alzheimer's patients using Deep machine learningWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2021Year of publication
- Publication date
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2021-06-12Full publication date if available
- Authors
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Hadi Varmazyar, Hossein Yousefi-Banaem, Saber Malekzadeh, Nahideh GharehaghajiList of authors in order
- Landing page
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https://arxiv.org/abs/2106.06743Publisher landing page
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https://arxiv.org/pdf/2106.06743Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
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greenOpen access status per OpenAlex
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https://arxiv.org/pdf/2106.06743Direct OA link when available
- Concepts
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Alzheimer's disease, Magnetic resonance imaging, Hippocampus, Dementia, Neuroimaging, Segmentation, Sørensen–Dice coefficient, Artificial intelligence, Computer science, Pattern recognition (psychology), Medicine, Disease, Neuroscience, Image segmentation, Psychology, Pathology, RadiologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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1Total citation count in OpenAlex
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2022: 1Per-year citation counts (last 5 years)
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27Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.onset | 42 |
| abstract_inverted_index.prone | 18 |
| abstract_inverted_index.stage | 211 |
| abstract_inverted_index.study | 59 |
| abstract_inverted_index.train | 118, 173 |
| abstract_inverted_index.using | 35, 74 |
| abstract_inverted_index.value | 161, 170 |
| abstract_inverted_index.which | 181 |
| abstract_inverted_index.(ADNI) | 112 |
| abstract_inverted_index.90.4%, | 164 |
| abstract_inverted_index.92.3%, | 155 |
| abstract_inverted_index.96.5%, | 158 |
| abstract_inverted_index.aging. | 15 |
| abstract_inverted_index.before | 40 |
| abstract_inverted_index.faster | 49 |
| abstract_inverted_index.images | 70, 99 |
| abstract_inverted_index.manual | 134 |
| abstract_inverted_index.method | 130 |
| abstract_inverted_index.model, | 123 |
| abstract_inverted_index.neural | 84 |
| abstract_inverted_index.stages | 24 |
| abstract_inverted_index.volume | 206 |
| abstract_inverted_index.changes | 20, 34, 207 |
| abstract_inverted_index.desired | 143 |
| abstract_inverted_index.disease | 2, 45, 109, 199 |
| abstract_inverted_index.imaging | 38 |
| abstract_inverted_index.machine | 76 |
| abstract_inverted_index.method. | 78 |
| abstract_inverted_index.network | 85 |
| abstract_inverted_index.segment | 89 |
| abstract_inverted_index.Methods: | 79 |
| abstract_inverted_index.Results: | 141 |
| abstract_inverted_index.achieved | 145 |
| abstract_inverted_index.approach | 187 |
| abstract_inverted_index.compared | 132 |
| abstract_inverted_index.dataset, | 113 |
| abstract_inverted_index.dementia | 13 |
| abstract_inverted_index.disease. | 27, 214 |
| abstract_inverted_index.disorder | 7 |
| abstract_inverted_index.extended | 193 |
| abstract_inverted_index.learning | 77 |
| abstract_inverted_index.magnetic | 36, 67 |
| abstract_inverted_index.metrics. | 140 |
| abstract_inverted_index.obtained | 180 |
| abstract_inverted_index.patients | 73, 105 |
| abstract_inverted_index.positive | 159 |
| abstract_inverted_index.proposed | 87, 129, 186 |
| abstract_inverted_index.Detection | 28 |
| abstract_inverted_index.available | 106 |
| abstract_inverted_index.measures. | 53 |
| abstract_inverted_index.measuring | 137 |
| abstract_inverted_index.predicted | 160 |
| abstract_inverted_index.prognosis | 196 |
| abstract_inverted_index.promising | 189 |
| abstract_inverted_index.resonance | 37, 68 |
| abstract_inverted_index.Alzheimers | 1, 26, 44, 72, 108, 198 |
| abstract_inverted_index.Initiative | 111 |
| abstract_inverted_index.Objective: | 54 |
| abstract_inverted_index.prediction | 202 |
| abstract_inverted_index.preventive | 50 |
| abstract_inverted_index.similarity | 139, 151 |
| abstract_inverted_index.Background: | 0 |
| abstract_inverted_index.Conclusion: | 184 |
| abstract_inverted_index.Hippocampus | 16 |
| abstract_inverted_index.acceptable. | 183 |
| abstract_inverted_index.coefficient | 152 |
| abstract_inverted_index.hippocampus | 33, 65, 91, 205 |
| abstract_inverted_index.iterations. | 148 |
| abstract_inverted_index.observation | 30 |
| abstract_inverted_index.performance | 126 |
| abstract_inverted_index.progressive | 5 |
| abstract_inverted_index.sensitivity | 156 |
| abstract_inverted_index.therapeutic | 52 |
| abstract_inverted_index.Intersection | 166 |
| abstract_inverted_index.Neuroimaging | 110 |
| abstract_inverted_index.architecture | 81 |
| abstract_inverted_index.segmentation | 62, 135, 144 |
| abstract_inverted_index.convolutional | 83 |
| abstract_inverted_index.respectively. | 124 |
| abstract_inverted_index.neurodegenerative | 6 |
| cited_by_percentile_year.max | 94 |
| cited_by_percentile_year.min | 89 |
| countries_distinct_count | 0 |
| institutions_distinct_count | 4 |
| citation_normalized_percentile.value | 0.43144333 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |