A5027453577- Cell Image Analysis Techniques
- Genetics, Bioinformatics, and Biomedical Research
- AI in cancer detection
- Innovative Teaching and Learning Methods
- Image Processing Techniques and Applications
- Advanced Fluorescence Microscopy Techniques
- Gene expression and cancer classification
- Impact of Technology on Adolescents
- Motivation and Self-Concept in Sports
École Polytechnique Fédérale de Lausanne
2020-2024
The identification of cell borders ('segmentation') in microscopy images constitutes a bottleneck for large-scale experiments. For the model organism Saccharomyces cerevisiae, current segmentation methods face challenges when cells bud, crowd, or exhibit irregular features. We present convolutional neural network (CNN) named YeaZ, underlying training set high-quality segmented yeast (>10 000 cells) including mutants, stressed cells, and time courses, as well graphical user interface web...
Abstract The processing of microscopy images constitutes a bottleneck for large-scale experiments. A critical step is the establishment cell borders (‘segmentation’), which required range applications such as growth or fluorescent reporter measurements. For model organism budding yeast ( Saccharomyces cerevisiae ), number methods segmentation exist. However, in experiments involving multiple cycles, stress, various mutants, cells crowd exhibit irregular visible features, necessitate frequent...