| Autor: |
Heemoon Yoon, Mira Park, Soonja Yeom, Matthew T. K. Kirkcaldie, Peter Summons, Sang-Hee Lee |
| Jazyk: |
angličtina |
| Rok vydání: |
2021 |
| Předmět: |
|
| Zdroj: |
IEEE Access, Vol 9, Pp 161926-161936 (2021) |
| Druh dokumentu: |
article |
| ISSN: |
2169-3536 |
| DOI: |
10.1109/ACCESS.2021.3132401 |
| Popis: |
Identification of amyloid beta ( $\text{A}\beta$ ) plaques in the cerebral cortex in models of Alzheimer’s Disease (AD) is of critical importance for research into therapeutics. Here we propose an innovative framework which automatically measures $\text{A}\beta $ plaques in the cortex of a rodent model, based on anatomical segmentation using a deep learning approach. The framework has three phases: data acquisition to enhance image quality using preprocessing techniques and image normalization with a novel plaque removal algorithm, then an anatomical segmentation phase using the trained model, and finally an analysis phase to quantitate $\text{A}\beta $ plaques. Supervised training with 946 sets of mouse brain section annotations exhibiting $\text{A}\beta $ protein-labeled plaques ( $\text{A}\beta $ plaques) were trained with deep neural networks (DNNs). Five DNN architectures: FCN32, FCN16, FCN8, SegNet, and U-Net, were tested. Of these, U-Net was selected as it showed the most reliable segmentation performance. The framework demonstrated an accuracy of 83.98% and 91.21% of the Dice coefficient score for atlas segmentation with the test dataset. The proposed framework automatically segmented the somatosensory cortex and calculated the intensity and extent of $\text{A}\beta $ plaques. This study contributes to image analysis in the field of neuroscience, allowing region-specific quantitation of image features using a deep learning approach. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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