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

Ke Yi; Han Li; Cheng Xu; Guoqing Zhong; Zhiquan Ding; Guolong Zhang; Xiaohui Guan; Meiling Zhong; Guanghui Li; Nan Jiang; Yuejin Zhang

doi:10.1016/j.compbiomed.2023.106906

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

Comput Biol Med. 2023 Jun:159:106906. doi: 10.1016/j.compbiomed.2023.106906. Epub 2023 Apr 13.

Authors

Ke Yi¹, Han Li², Cheng Xu¹, Guoqing Zhong¹, Zhiquan Ding¹, Guolong Zhang¹, Xiaohui Guan³, Meiling Zhong⁴, Guanghui Li¹, Nan Jiang¹, Yuejin Zhang⁵

Affiliations

¹ School of Information Engineering, East China Jiaotong University, 330013 Nanchang, Jiangxi, China.
² Meta Platforms, Inc., Menlo Park, CA 94025, USA.
³ The National Engineering Research Center for Bioengineering Drugs and the Technologies, Nanchang University, Nanchang, China.
⁴ School of Materials Science and Engineering, East China Jiaotong University, Nanchang, China.
⁵ School of Information Engineering, East China Jiaotong University, 330013 Nanchang, Jiangxi, China. Electronic address: zyjecjtu@foxmail.com.

PMID: 37084638
DOI: 10.1016/j.compbiomed.2023.106906

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.

Keywords: Deep learning; Different stages; Differentiation; Induced ADSCs; Morphological features; Recognition.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Cell Differentiation / physiology
Deep Learning*
Diagnostic Imaging
Image Processing, Computer-Assisted / methods
Neural Networks, Computer