ד"ר איתי ספקטור :Advanced Histology, Cytology and Machine learning based Quantitative Image Analysis in Pre-clinical Research

:Abstract

Histology and Cytology play a critical role in biomedical research by enabling the detection and analysis of tissue and cell- Morphology, abnormalities, protein and RNA expression and treatments effects. Over the past decade, significant advancements have transformed this field, including the development of synthetic biomarkers and new methods that enable the use of multiple fluorescence-labeled antibodies and RNA probes on single sample section. Additionally, innovations in confocal microscopy and high-resolution slides scanning microscopes have greatly improved imaging capabilities. The emergence of Machine learning based Quantitative Image Analysis- that enables precise analysis of histology and cytology large datasets (i.e. cell populations, distances between cell populations, expression level in each cell population), patterns recognition (i.e. blood vessels populations detection and analysis, neuronal and collagen fibers parameters analysis etc.).
Together- these advancements in Histology and cytology, microscopy and image analysis- enable researchers to extract high quantity of data from each sample section, enabling accurate quantitative evaluation of basic research data and treatments effects.
In this seminar, I will present the main Histology, Cytology and Machine learning based Quantitative Image Analysis (HCA) methodologies and innovations in relevant research fields, providing examples in different cells, tissues and animal models. This will equip researchers with a better understanding of .how to apply these cutting-edge HCA techniques to their own .studies