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VirtualStain – Artificial intelligence solutions to virtually stain label-free cell and tissue images for studying cardiovascular diseases of fish and mammals

VirtualStain will develop artificial intelligence (AI) tools to process, label and analyse microscopy and nanoscopy images of tissues and cells. This will make the time-consuming task of chemically staining (with noxious chemicals) such images obsolete. Applying AI to such a task will also enable researcher to image and label living tissues and cells, and follow them in real-time. New insights provided on tissue and cell function through these enhanced labelling and monitoring processes will allow for the development of complex and dynamic AI models of tissue and cells systems for use in medical research.


Start: January 01. 2020
End: December 31. 2025
Unit: Department of Computer Science

Project categories: Applied Research
Academic disciplines: Physics / Cell biology / Computer technology
Keywords: Microscopy / Data analysis




Funding:

UiT The Arctic University of Norway


Participants:

Ludwig Alexander Horsch
Dilip Kumar Prasad
Frank Melandsø
Krishna Agarwal
Truls Myrmel
Åsa Birna Birgisdottir
Roy Ambli Dalmo
Jaya Kumari Swain


Results:

  1. 3DSIM data of mitochondria in the cardiomyoblast cell-line H9c2 adapted to either glucose or galactose (Other product)
  2. Replication Data for: Physics based machine learning for sub-cellular segmentation in living cells (Other product)
  3. Visualizing and quantifying mitochondria-derived vesicles in cardiomyoblasts (Academic lecture)
  4. Mitochondrial dynamics and quantification of mitochondria-derived vesicles in cardiomyoblasts using structured illumination microscopy (Academic article)
  5. Physics-based machine learning for subcellular segmentation in living cells (Academic article)
  6. Digital Staining of Mitochondria in Label-free Live-cell Microscopy (Academic chapter/article/Conference paper)
  7. Performance improvement in deep learning models for outdoor semantic segmentation for autonomous driving for unstructured environment (Academic lecture)
  8. Dataset publication: fluorescence imaging of salmon skin cells illuminated by multimodal waveguide chip (Popular scientific article)
  9. New publication: New chip-based microscopy technique enables super-resolution images over large areas to be captured of salmon skin cells (Popular scientific article)
  10. PhD defence: Bringing optical nanoscopy to life – Super-resolution microscopy of living cells (Popular scientific article)
  11. Salmon keratocyte DIC microscopy videos (Other product)
  12. Quantification of mitochondria-derived vesicles in cardiomyoblasts using structured illumination microscopy and machine learning-based segmentation (Lecture)
  13. Quantification of mitochondria-derived vesicles in cardiomyoblasts using structured illumination microscopy and machine learning-based segmentation (Academic lecture)
  14. Quantification of mitochondria‐derived vesicles in cardiomyoblasts using 3DSIM & machine learning-based segmentation (Poster)
  15. Multidisciplinary biophotonics, open science, and … plug & pray deep learning? (Academic article)