Journal
JOURNAL OF CLINICAL MEDICINE
Volume 8, Issue 8, Pages -Publisher
MDPI
DOI: 10.3390/jcm8081159
Keywords
T-cell; nuclei; phase image; fluorescent imaging; instance segmentation
Categories
Funding
- CPRIT [RP180466]
- MRA Established Investigator Award [509800]
- NSF [1705464]
- CDMRP [CA160591]
- Owens foundation
- Directorate For Engineering
- Div Of Chem, Bioeng, Env, & Transp Sys [1705464] Funding Source: National Science Foundation
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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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