论文标题
通过深度学习对脑器官的自动定量分析
Automatic Quantitative Analysis of Brain Organoids via Deep Learning
论文作者
论文摘要
脑器官技术的最新进展是令人兴奋的新方法，它有可能改变医生和研究人员理解和治疗脑疾病的方式。尽管在新药物测试，疾病建模和科学研究中源自人类干细胞的脑器官的明显使用，但仍在大量耗时的工作来观察和分析人类内部的内部结构，细胞和神经，特别是没有标准的定量分析方法结合了大脑器官生长的AI技术。在本文中，提出了一种自动计算机辅助分析方法，该方法针对用不同的荧光标记的脑类器官切片通道。我们在两个组显微镜图像的两个通道上应用了该方法，实验结果显示了野生型和突变型大脑器官之间的明显差异。
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.
