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.

中文翻译：

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基于学习的信息融合的阻抗-光学双模细胞培养成像

虽然电阻抗断层扫描 (EIT) 已发现许多生物医学应用，但需要更好的图像质量来为组织工程和再生医学提供定量分析。本文报告了一种阻抗-光学双模成像框架，主要针对高质量的 3D 细胞培养成像，并可扩展到其他组织工程应用。该框架包括三个组件，即阻抗-光学双模态传感器、引导图像处理算法和用于信息融合的名为多尺度特征交叉融合网络（MSFCF-Net）的深度学习模型。MSFCF-Net有两个输入，即EIT测量和引导图像处理算法生成的二进制掩模图像，其输入是RGB显微图像。然后，该网络有效地融合来自两种不同成像模式的信息并生成最终的电导率图像。我们通过数值模拟和 MCF-7 细胞成像实验来评估所提出的双模态框架的性能。结果表明，所提出的方法可以显着提高图像质量，表明阻抗-光学联合成像具有同时揭示组织级目标结构和功能信息的潜力。