Hybrid Learning-Based Cell Aggregate Imaging With Miniature Electrical Impedance Tomography
نویسندگان
چکیده
Real-time, nondestructive, and label-free imaging of 3-D cell culture process using miniature Electrical Impedance Tomography (mEIT) is an emerging topic in tissue engineering. Image reconstruction mEIT for challenging due to weak sensing signals increased sensitivity sensor imperfection. Conventional regularization-based image methods cannot always achieve satisfactory performance terms quality computational efficiency this particular setup. Recent advances deep learning have pointed out a promising alternative. However, with single neural network, it still difficult reconstruct multiple objects varying conductivity levels; these cases are widespread the application imaging. Aiming at challenge, article, we propose group sparsity (GS) hybrid algorithm mEIT. A network proposed estimate structural information form binary masks given limited amount data sets. Then, encoded GS regularization obtain final estimation conductivity. The approach validated by both simulation experimental on MCF-7 human breast cancer aggregates, which demonstrates its superior generalization ability compared number existing algorithms.
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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.2020.3035384