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Sensors 2018, 18(2), 447;

Visual Estimation of Bacterial Growth Level in Microfluidic Culture Systems

Robotics Program, Korea Advanced Institute of Science and Technology, 291 Daehak-ro, Daejeon 34141, Korea
Department of Mechanical Engineering, Korea Advanced Institute of Science and Technology, 291 Daehak-ro, Daejeon 34141, Korea
KAIST Institute for Health Science and Technology, 291 Daehak-ro, Daejeon 34141, Korea
Author to whom correspondence should be addressed.
Received: 2 January 2018 / Revised: 27 January 2018 / Accepted: 31 January 2018 / Published: 3 February 2018
(This article belongs to the Special Issue Lab-on-a-Chip–From Point of Care to Precision Medicine)
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Microfluidic devices are an emerging platform for a variety of experiments involving bacterial cell culture, and has advantages including cost and convenience. One inevitable step during bacterial cell culture is the measurement of cell concentration in the channel. The optical density measurement technique is generally used for bacterial growth estimation, but it is not applicable to microfluidic devices due to the small sample volumes in microfluidics. Alternately, cell counting or colony-forming unit methods may be applied, but these do not work in situ; nor do these methods show measurement results immediately. To this end, we present a new vision-based method to estimate the growth level of the bacteria in microfluidic channels. We use Fast Fourier transform (FFT) to detect the frequency level change of the microscopic image, focusing on the fact that the microscopic image becomes rough as the number of cells in the field of view increases, adding high frequencies to the spectrum of the image. Two types of microfluidic devices are used to culture bacteria in liquid and agar gel medium, and time-lapsed images are captured. The images obtained are analyzed using FFT, resulting in an increase in high-frequency noise proportional to the time passed. Furthermore, we apply the developed method in the microfluidic antibiotics susceptibility test by recognizing the regional concentration change of the bacteria that are cultured in the antibiotics gradient. Finally, a deep learning-based data regression is performed on the data obtained by the proposed vision-based method for robust reporting of data. View Full-Text
Keywords: Lab-on-a-chip; 3D Printing; microfluidics; solid modeling Lab-on-a-chip; 3D Printing; microfluidics; solid modeling

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Kim, K.; Kim, S.; Jeon, J.S. Visual Estimation of Bacterial Growth Level in Microfluidic Culture Systems. Sensors 2018, 18, 447.

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