Deep Learning Based Cell Imaging with Electrical Impedance Tomography

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract / Description of output

Monitoring the 3-D cell culture process or drug responses non-destructively using Electrical Impedance Tomography (EIT) is an emerging topic in biomedical imaging. Significant efforts have been spent on developing EIT image reconstruction algorithms in order to achieve robust and high-quality cell imaging. The considerable computation time and imperfect image quality are the main issues of these conventional methods whereas the emergence of deep learning techniques point out a new direction due to its fast inferences on object detection, image segmentation and classification. In this paper, a novel deep learning architecture is proposed by adding a fully connected layer before a U-Net structure. This new architecture will first generate an initial guess of the conductivity distribution and then feed it to the following denoising model. A novel initialization strategy is also proposed to further help obtain this initial guess. The performance of the method is verified by simulation and experimental data. The results show that the proposed model outperforms the state-of-the-art EIT algorithms and can generalize well to reconstruct unseen cases consisting of human breast cancer cell pellet.
Original languageEnglish
Title of host publication2020 IEEE Instrumentation and Measurement Technology Conference (I2MTC)
PublisherInstitute of Electrical and Electronics Engineers
ISBN (Electronic)978-1-7281-4460-3
ISBN (Print)978-1-7281-4461-0
DOIs
Publication statusE-pub ahead of print - 30 Jun 2020
Event2020 IEEE International Instrumentation and Measurement Technology Conference - Dubrovnik, Dubrovnik, Croatia
Duration: 25 May 202028 May 2020
https://i2mtc2020.ieee-ims.org/

Conference

Conference2020 IEEE International Instrumentation and Measurement Technology Conference
Abbreviated titleI2MTC2020
Country/TerritoryCroatia
CityDubrovnik
Period25/05/2028/05/20
Internet address

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