Machine Learning Paper in Computational and Structural Biotechnology Reports

Congratulations to Filippo and Arash for leading this work, “Machine learning driven image segmentation and shape clustering of algal microscopic images obtained from various water types”.

Accurate identification and quantification of algae and cyanobacteria are vital for ecological research, water quality monitoring, and public health safety. However, traditional methods of manually counting and morphologically identifying these microorganisms are time-consuming and prone to human error. This work, published in Computational and Structural Biotechnology Reports uses Machine Learning to automate cell identification and enumeration from microscopy images.