| Autor: |
Sabina Stefan, Anna Kim, Paul J. Marchand, Frederic Lesage, Jonghwan Lee |
| Jazyk: |
angličtina |
| Rok vydání: |
2022 |
| Předmět: |
|
| Zdroj: |
Frontiers in Neuroscience, Vol 16 (2022) |
| Druh dokumentu: |
article |
| ISSN: |
1662-453X |
| DOI: |
10.3389/fnins.2022.835773 |
| Popis: |
We present a deep learning and simulation-based method to measure cortical capillary red blood cell (RBC) flux using Optical Coherence Tomography (OCT). This method is more accurate than the traditional peak-counting method and avoids any user parametrization, such as a threshold choice. We used data that was simultaneously acquired using OCT and two-photon microscopy to uncover the distribution of parameters governing the height, width, and inter-peak time of peaks in OCT intensity associated with the passage of RBCs. This allowed us to simulate thousands of time-series examples for different flux values and signal-to-noise ratios, which we then used to train a 1D convolutional neural network (CNN). The trained CNN enabled robust measurement of RBC flux across the entire network of hundreds of capillaries. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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