Kristina Ulicna , PhD

Kristina holds a BSc in Biomedical Sciences from King’s College London and is currently working on her PhD at University College London with Alan Lowe and Guillaume Charras. Her PhD research utilizes deep learning to track single cell heterogeneity within non-cancer and cancer cell lines. Kristina’s work in this area was recently accepted to Frontiers in Computer Science: https://www.frontiersin.org/articles/10.3389/fcomp.2021.734559/abstract

She also recently completed an internship at Microsoft Research Cambridge as an AI research scientist and Forbes Slovensko shortlisted her as a 2021’s under-30 laureate.

More about her work: https://github.com/KristinaUlicna

Separation of cells based on their tracking status: A colourised binary mask of a time-lapse microscopy field of view of medium confluency with individual cells highlighted as survivors if they can be tracked since the initial movie frame (cyan), incomers if they migrated into the field of view throughout the movie (yellow) or mistracks if an error occurred in the automated trajectory reconstruction (red).

使用深度学习技术追踪单细胞

人工智能解决方案在显微镜领域的应用不断拓展。从自动化目标分类到虚拟染色，机器学习和深度学习技术在帮助显微镜学家简化分析工作的同时，也在持续推动科学技术领域的突破。