Impedance-optical Dual-modal Cell Culture Imaging with Learning-based Information Fusion

Zhe Liu, Pierre Bagnaninchi, Yunjie Yang*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


While Electrical Impedance Tomography (EIT) has found many biomedicine applications, better image quality is needed to provide quantitative analysis for tissue engineering and regenerative medicine. This paper reports an impedance-optical dual-modal imaging framework that primarily targets at high-quality 3D cell culture imaging and can be extended to other tissue engineering applications. The framework comprises three components, i.e., an impedance-optical dual-modal sensor, the guidance image processing algorithm, and a deep learning model named multi-scale feature cross fusion network (MSFCF-Net) for information fusion. The MSFCF-Net has two inputs, i.e., the EIT measurement and a binary mask image generated by the guidance image processing algorithm, whose input is an RGB microscopic image. The network then effectively fuses the information from the two different imaging modalities and generates the final conductivity image. We assess the performance of the proposed dual-modal framework by numerical simulation and MCF-7 cell imaging experiments. The results show that the proposed method could improve the image quality notably, indicating that impedance-optical joint imaging has the potential to reveal the structural and functional information of tissue-level targets simultaneously.
Original languageEnglish
Pages (from-to)1-1
JournalIEEE Transactions on Medical Imaging
Early online date19 Nov 2021
Publication statusE-pub ahead of print - 19 Nov 2021


  • Cell culture
  • dual-modal imaging
  • electrical impedance tomography
  • deep learning
  • image processing
  • Imaging
  • image reconstruction
  • Electrodes
  • Conductivity
  • Three-dimensional displays


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