A Novel Deep Learning Approach with a 3D Convolutional Ladder Network for Differential Diagnosis of Idiopathic Normal Pressure Hydrocephalus and Alzheimer’s Disease
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- Irie Ryusuke
- Department of Radiology, Juntendo University School of Medicine Department of Radiology, Graduate School of Medicine, The University of Tokyo
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- Otsuka Yujiro
- Department of Radiology, Juntendo University School of Medicine Milliman, Inc.
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- Hagiwara Akifumi
- Department of Radiology, Juntendo University School of Medicine Department of Radiology, Graduate School of Medicine, The University of Tokyo
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- Kamagata Koji
- Department of Radiology, Juntendo University School of Medicine
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- Kamiya Kouhei
- Department of Radiology, Graduate School of Medicine, The University of Tokyo
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- Suzuki Michimasa
- Department of Radiology, Juntendo University School of Medicine
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- Wada Akihiko
- Department of Radiology, Juntendo University School of Medicine
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- Maekawa Tomoko
- Department of Radiology, Juntendo University School of Medicine Department of Radiology, Graduate School of Medicine, The University of Tokyo
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- Fujita Shohei
- Department of Radiology, Juntendo University School of Medicine Department of Radiology, Graduate School of Medicine, The University of Tokyo
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- Kato Shimpei
- Department of Radiology, Juntendo University School of Medicine Department of Radiology, Graduate School of Medicine, The University of Tokyo
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- Nakajima Madoka
- Department of Neurosurgery, Juntendo University School of Medicine
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- Miyajima Masakazu
- Department of Neurosurgery, Juntendo Tokyo Koto Geriatric Medical Center
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- Motoi Yumiko
- Department of Neurology, Juntendo University School of Medicine Department of Diagnosis, Prevention and Treatment of Dementia, Juntendo University Graduate School of Medicine
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- Abe Osamu
- Department of Radiology, Graduate School of Medicine, The University of Tokyo
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- Aoki Shigeki
- Department of Radiology, Juntendo University School of Medicine
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Abstract
<p>Purpose: Idiopathic normal pressure hydrocephalus (iNPH) and Alzheimer’s disease (AD) are geriatric diseases and common causes of dementia. Recently, many studies on the segmentation, disease detection, or classification of MRI using deep learning have been conducted. The aim of this study was to differentiate iNPH and AD using a residual extraction approach in the deep learning method.</p><p>Methods: Twenty-three patients with iNPH, 23 patients with AD and 23 healthy controls were included in this study. All patients and volunteers underwent brain MRI with a 3T unit, and we used only whole-brain three-dimensional (3D) T1-weighted images. We designed a fully automated, end-to-end 3D deep learning classifier to differentiate iNPH, AD and control. We evaluated the performance of our model using a leave-one-out cross-validation test. We also evaluated the validity of the result by visualizing important areas in the process of differentiating AD and iNPH on the original input image using the Gradient-weighted Class Activation Mapping (Grad-CAM) technique.</p><p>Results: Twenty-one out of 23 iNPH cases, 19 out of 23 AD cases and 22 out of 23 controls were correctly diagnosed. The accuracy was 0.90. In the Grad-CAM heat map, brain parenchyma surrounding the lateral ventricle was highlighted in about half of the iNPH cases that were successfully diagnosed. The medial temporal lobe or inferior horn of the lateral ventricle was highlighted in many successfully diagnosed cases of AD. About half of the successful cases showed nonspecific heat maps.</p><p>Conclusion: Residual extraction approach in a deep learning method achieved a high accuracy for the differential diagnosis of iNPH, AD, and healthy controls trained with a small number of cases.</p>
Journal
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- Magnetic Resonance in Medical Sciences
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Magnetic Resonance in Medical Sciences 19 (4), 351-358, 2020
Japanese Society for Magnetic Resonance in Medicine