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Traditionally, condition monitoring of mammalian cell culture processes is based on sampling and off-line analysis, which is labour intensive, time consuming, and causes time delays. In this work, in situ microscope and on-line Raman spectroscopy are investigated for simultaneous measurement of multiple properties of the cell growth state and biochemical indices of suspended animal cells. The focus is on investigation of deep learning-based Mask R-CNN algorithm for image analysis. The model is trained by 184 images with 183,040 cells using data augmentation methods and transfer learning technique. Mask R-CNN segments the clustered cells more effectively than the conventional one combining edge detection, intensity thresholding, and advanced watershed method. The evolution of geometrical features of cells is further analyzed, including equivalent diameter, circularity, and aspect ratio. It demonstrates the great potential of deep learning in the analysis of on-line images for control of the cell culture process.

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Integration of Raman Spectroscopy, On-Line Microscopic Imaging and Deep Learning-Based Image Analysis for Real-Time Monitoring of Cell Culture Process

  • 会议论文

  • 发表日期：

    2025年

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  • Xiaoli Wang,
  • Guangzheng Zhou,
  • Xue Zhong Wang

摘要

Traditionally, condition monitoring of mammalian cell culture processes is based on sampling and off-line analysis, which is labour intensive, time consuming, and causes time delays. In this work, in situ microscope and on-line Raman spectroscopy are investigated for simultaneous measurement of multiple properties of the cell growth state and biochemical indices of suspended animal cells. The focus is on investigation of deep learning-based Mask R-CNN algorithm for image analysis. The model is trained by 184 images with 183,040 cells using data augmentation methods and transfer learning technique. Mask R-CNN segments the clustered cells more effectively than the conventional one combining edge detection, intensity thresholding, and advanced watershed method. The evolution of geometrical features of cells is further analyzed, including equivalent diameter, circularity, and aspect ratio. It demonstrates the great potential of deep learning in the analysis of on-line images for control of the cell culture process.