Improved senescent cell segmentation on bright-field microscopy images exploiting representation level contrastive learning

ÇELEBİ F., Boyvat D., Ayaz-Guner S., Tasdemir K., İÇÖZ K.

International Journal of Imaging Systems and Technology, cilt.34, sa.2, 2024 (SCI-Expanded) identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 34 Sayı: 2
  • Basım Tarihi: 2024
  • Doi Numarası: 10.1002/ima.23052
  • Dergi Adı: International Journal of Imaging Systems and Technology
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, PASCAL, Applied Science & Technology Source, Biotechnology Research Abstracts, Compendex, INSPEC
  • Anahtar Kelimeler: cellular senescence, instance segmentation, mask R-CNN, microscopy images, self-supervised learning, SimCLR
  • Abdullah Gül Üniversitesi Adresli: Evet

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 time-consuming 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.