Significant Dimension Reduction of 3D Brain MRI using 3D Convolutional Autoencoders
| Autor: | Hitoshi Iyatomi, Hayato Arai, Yusuke Chayama, Kenichi Oishi |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Databases
Factual Computer science Feature extraction Iterative reconstruction computer.software_genre 030218 nuclear medicine & medical imaging Reduction (complexity) 03 medical and health sciences 0302 clinical medicine Voxel medicine Image retrieval Neuroradiology medicine.diagnostic_test business.industry Dimensionality reduction Brain Magnetic resonance imaging Pattern recognition Effective dimension Magnetic Resonance Imaging Visualization Artificial intelligence business computer 030217 neurology & neurosurgery |
| Zdroj: | EMBC |
| ISSN: | 2694-0604 |
| Popis: | Content-based image retrieval (CBIR) is a technology designed to retrieve images from a database based on visual features. While the CBIR is highly desired, it has not been applied to clinical neuroradiology, because clinically relevant neuroradiological features are swamped by a huge number of noisy and unrelated voxel information. Thus, effective dimension reduction is the key to successful CBIR. We propose a novel dimensional compression method based on 3D convolutional autoencoders (3D-CAE), which was applied to the ADNI2 3D brain MRI dataset. Our method succeeded in compressing 5 million voxel information to only 150 dimensions, while preserving clinically relevant neuroradiological features. The RMSE per voxel was as low as 8.4%, suggesting a promise of our method toward the application to the CBIR. |
| Databáze: | OpenAIRE |
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