Digital Twin Modeling and Simulation of Distributed and Centralized Integrated Energy System on CloudPSS-IESLab

Zhou, Qiang; Xiang, Yue; Song, Yankan; Chen, Ying; Shi, Zhen; Yang, Ming

doi:10.1109/ispec50848.2020.9351032

Cited by 5 publications

(1 citation statement)

References 13 publications

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“…Stem cells are cells with unlimited or immortal self-renewal capacity, which can produce at least one type, highly differentiated progeny cells, so they are called “universal cells” in the medical profession [ 9 – 11 ]. According to the classification of developmental stages, stem cells can be divided into embryonic stem cells and adult stem cells [ 12 – 14 ].…”

Section: Theories and Methodsmentioning

confidence: 99%

Segmentation, Detection, and Tracking of Stem Cell Image by Digital Twins and Lightweight Deep Learning

Liu²,

Sun

2022

Computational Intelligence and Neuroscience

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The current work aims to strengthen the research of segmentation, detection, and tracking methods of stem cell image in the fields of regenerative medicine and tissue damage restoration. Firstly, based on the relevant theories of stem cell image segmentation, digital twins (DTs), and lightweight deep learning, a new phase contrast microscope is introduced through the research of optical microscope. Secondly, the results of DTs method and phase contrast imaging principle are compared in stem cell image segmentation and detection. Finally, a lightweight deep learning model is introduced in the segmentation and tracking of stem cell image to observe the gray value and mean value before and after stem cell image movement and stem cell division. The results show that phase contrast microscope can increase the phase contrast and amplitude difference of stem cell image and solve the problem of stem cell image segmentation to a certain extent. The detection results of DTs method are compared with phase contrast imaging principle. It indicates that not only can DTs method make the image contour more accurate and clearer, but also its accuracy, recall, and F1 score are 0.038, 0.024, and 0.043 higher than those of the phase contrast imaging method. The lightweight deep learning model is applied to the segmentation and tracking of stem cell image. It is found that the gray value and mean value of stem cell image before and after movement and stem cell division do not change significantly. Hence, the application of DTs and lightweight deep learning methods in the segmentation, detection, and tracking of stem cell image has great reference significance for the development of biology and medicine.

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