Structure-Aware Dual-Branch Network for Electrical Impedance Tomography in Cell Culture Imaging
نویسندگان
چکیده
Electrical impedance tomography (EIT) is an emerging imaging modality to monitor 3D cell culture dynamics through reconstructing the electrical properties of clusters. Recently, machine-learning (ML)-based approaches have achieved significant gains for image reconstruction EIT against conventional physical model-based methods. However, continuous, multilevel conductivity distributions, which commonly exists in imaging, are more rigorous reconstruct and remains challenging. This article aims tackle this challenge by proposing a structure-aware dual-branch deep-learning method predict both structure distribution values. The proposed network comprises two independent branches encode features, respectively. jointed later make final predictions distributions. Numerical experimental evaluation results demonstrate superior performance dealing with multilevel, continuous problem.
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ژورنال
عنوان ژورنال: IEEE Transactions on Instrumentation and Measurement
سال: 2021
ISSN: ['1557-9662', '0018-9456']
DOI: https://doi.org/10.1109/tim.2021.3092524