تقرير
Automatic Quantitative Analysis of Brain Organoids via Deep Learning
| العنوان: | Automatic Quantitative Analysis of Brain Organoids via Deep Learning |
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| المؤلفون: | Shi, Jingli |
| سنة النشر: | 2022 |
| المجموعة: | Computer Science Quantitative Biology |
| مصطلحات موضوعية: | Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Artificial Intelligence, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, Quantitative Biology - Quantitative Methods |
| الوصف: | Recent advances in brain organoid technology are exciting new ways, which have the potential to change the way how doctors and researchers understand and treat cerebral diseases. Despite the remarkable use of brain organoids derived from human stem cells in new drug testing, disease modeling, and scientific research, it is still heavily time-consuming work to observe and analyze the internal structure, cells, and neural inside the organoid by humans, specifically no standard quantitative analysis method combined growing AI technology for brain organoid. In this paper, an automated computer-assisted analysis method is proposed for brain organoid slice channels tagged with different fluorescent. We applied the method on two channels of two group microscopy images and the experiment result shows an obvious difference between Wild Type and Mutant Type cerebral organoids. |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2211.00750 |
| رقم الأكسشن: | edsarx.2211.00750 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |