Morphological feature recognition of different differentiation stages of induced ADSCs based on deep learning.

Journal: Computers in biology and medicine
Published Date: Apr 13, 2023

Abstract

In order to accurately identify the morphological features of different differentiation stages of induced Adipose Derived Stem Cells (ADSCs) and judge the differentiation types of induced ADSCs, a morphological feature recognition method of different differentiation stages of induced ADSCs based on deep learning is proposed. Using the super-resolution image acquisition method of ADSCs differentiation based on stimulated emission depletion imaging, after obtaining the super-resolution images at different stages of inducing ADSCs differentiation, the noise of the obtained image is removed and the image quality is optimized through the ADSCs differentiation image denoising model based on low rank nonlocal sparse representation; The denoised image is taken as the recognition target of the morphological feature recognition method for ADSCs differentiation image based on the improved Visual Geometry Group (VGG-19) convolutional neural network. Through the improved VGG-19 convolutional neural network and class activation mapping method, the morphological feature recognition and visual display of the recognition results at different stages of inducing ADSCs differentiation are realized. After testing, this method can accurately identify the morphological features of different differentiation stages of induced ADSCs, and is available.

Authors

  • Ke Yi
    School of Information Engineering, East China Jiaotong University, 330013 Nanchang, Jiangxi, China.
  • Han Li
  • Cheng Xu
    School of Photovoltaic and Renewable Energy Engineering, University of New South Wales, 2052 Sydney, Australia.
  • Guoqing Zhong
    School of Information Engineering, East China Jiaotong University, 330013 Nanchang, Jiangxi, China.
  • Zhiquan Ding
    Sichuan Institute of Aerospace Electronic Equipment, Chengdu 610100, China. Electronic address: 13350314996@163.com.
  • Guolong Zhang
    School of Information Engineering, East China Jiaotong University, 330013 Nanchang, Jiangxi, China.
  • Xiaohui Guan
    The National Engineering Research Center for Bioengineering Drugs and the Technologies, Nanchang University, Nanchang, China.
  • Meiling Zhong
    School of Materials Science and Engineering, East China Jiaotong University, Nanchang, China.
  • Guanghui Li
    State Key Laboratory of Ultrasound in Medicine and Engineering, College of Biomedical Engineering, Chongqing Medical University, Yixueyuan Road, Yuzhong District, Chongqing, 400016, China.
  • Nan Jiang
  • Yuejin Zhang
    School of Information Engineering, East China Jiaotong University, 330013 Nanchang, Jiangxi, China. Electronic address: zyjecjtu@foxmail.com.

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