Convolutional Neural Networks with an Application on Human Induced Pluripotent Stem Cell Region Recognition Using Microscopy Images

• Main Authors: Cheng-Yu Lin, 林政宇
• Other Authors: Yuan-Hsiang Chang
• Format: Others
• Language: zh-TW
• Published: 2017
• Online Access: http://ndltd.ncl.edu.tw/handle/92385542870545928601

碩士 === 中原大學 === 資訊工程研究所 === 105 === We present a deep learning architecture Convolutional Neural Networks (CNNs) for automatic classification and recognition of reprogramming and reprogrammed human Induced Pluripotent Stem (iPS) cell regions in microscopy images. The differentiated cells that possibly undergo reprogramming to iPS cells can be detected by this method for screening reagents or culture conditions in iPS induction. The learning results demonstrate that our CNNs can achieve the Top-1 and Top-2 error rates of 5.9% and 0.9%, respectively, to produce probability maps for the automatic analysis. The implementation results show that this automatic method can successfully detect and localize the human iPS cell formation, thereby yield a potential tool for helping iPS cell culture.