Improved senescent cell segmentation on bright-field microscopy images exploiting representation level contrastive learning
Özet
Mesenchymal stem cells (MSCs) are stromal cells which have multi-lineage
differentiation and self-renewal potentials. Accurate estimation of total
number of senescent cells in MSCs is crucial for clinical applications. Traditional manual cell counting using an optical bright-field microscope is timeconsuming and needs an expert operator. In this study, the senescence cells
were segmented and counted automatically by deep learning algorithms.
However, well-performing deep learning algorithms require large numbers
of labeled datasets. The manual labeling is time consuming and needs an
expert. This makes deep learning-based automated counting process impractically expensive. To address this challenge, self-supervised learning based
approach was implemented. The approach incorporates representation level
contrastive learning component into the instance segmentation algorithm
for efficient senescent cell segmentation with limited labeled data. Test
results showed that the proposed model improves mean average precision
and mean average recall of downstream segmentation task by 8.3% and 3.4%
compared to original segmentation model.
