تقرير
Artificial neural networks for 3D cell shape recognition from confocal images
| العنوان: | Artificial neural networks for 3D cell shape recognition from confocal images |
|---|---|
| المؤلفون: | Simionato, G., Hinkelmann, K., Chachanidze, R., Bianchi, P., Fermo, E., van Wijk, R., Leonetti, M., Wagner, C., Kaestner, L., Quint, S. |
| سنة النشر: | 2020 |
| المجموعة: | Quantitative Biology |
| مصطلحات موضوعية: | Quantitative Biology - Quantitative Methods, Electrical Engineering and Systems Science - Image and Video Processing |
| الوصف: | We present a dual-stage neural network architecture for analyzing fine shape details from microscopy recordings in 3D. The system, tested on red blood cells, uses training data from both healthy donors and patients with a congenital blood disease. Characteristic shape features are revealed from the spherical harmonics spectrum of each cell and are automatically processed to create a reproducible and unbiased shape recognition and classification for diagnostic and theragnostic use. Comment: 17 pages, 8 figures |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2005.08040 |
| رقم الأكسشن: | edsarx.2005.08040 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |