Phasetime: Deep Learning Approach to Detect Nuclei in Time Lapse Phase Images
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Department of Electrical and Computer Engineering, Cullen College of Engineering, University of Houston, Houston, TX 77004, USA
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Department of Chemical and Biomolecular Engineering, Cullen College of Engineering, University of Houston, Houston, TX 77004, USA
*
Author to whom correspondence should be addressed.
J. Clin. Med. 2019, 8(8), 1159; https://doi.org/10.3390/jcm8081159
Received: 16 June 2019
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Revised: 29 July 2019
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Accepted: 30 July 2019
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Published: 2 August 2019
(This article belongs to the Special Issue The Future of Artificial Intelligence in Clinical Medicine)
Time lapse microscopy is essential for quantifying the dynamics of cells, subcellular organelles and biomolecules. Biologists use different fluorescent tags to label and track the subcellular structures and biomolecules within cells. However, not all of them are compatible with time lapse imaging, and the labeling itself can perturb the cells in undesirable ways. We hypothesized that phase image has the requisite information to identify and track nuclei within cells. By utilizing both traditional blob detection to generate binary mask labels from the stained channel images and the deep learning Mask RCNN model to train a detection and segmentation model, we managed to segment nuclei based only on phase images. The detection average precision is 0.82 when the IoU threshold is to be set 0.5. And the mean IoU for masks generated from phase images and ground truth masks from experts is 0.735. Without any ground truth mask labels during the training time, this is good enough to prove our hypothesis. This result enables the ability to detect nuclei without the need for exogenous labeling.
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Keywords:
T-cell; nuclei; phase image; fluorescent imaging; instance segmentation
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MDPI and ACS Style
Yuan, P.; Rezvan, A.; Li, X.; Varadarajan, N.; Van Nguyen, H. Phasetime: Deep Learning Approach to Detect Nuclei in Time Lapse Phase Images. J. Clin. Med. 2019, 8, 1159. https://doi.org/10.3390/jcm8081159
AMA Style
Yuan P, Rezvan A, Li X, Varadarajan N, Van Nguyen H. Phasetime: Deep Learning Approach to Detect Nuclei in Time Lapse Phase Images. Journal of Clinical Medicine. 2019; 8(8):1159. https://doi.org/10.3390/jcm8081159
Chicago/Turabian StyleYuan, Pengyu, Ali Rezvan, Xiaoyang Li, Navin Varadarajan, and Hien Van Nguyen. 2019. "Phasetime: Deep Learning Approach to Detect Nuclei in Time Lapse Phase Images" Journal of Clinical Medicine 8, no. 8: 1159. https://doi.org/10.3390/jcm8081159
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