Automatic Measurement

Automatic measurement leverages computer vision and machine learning, primarily deep learning, to automate the quantification of features in images and videos across diverse scientific domains. Current research focuses on applying convolutional neural networks (CNNs), including U-Net and its variants, to segment regions of interest and extract relevant measurements, improving accuracy and efficiency compared to manual methods. This automated approach significantly accelerates data analysis in fields ranging from medical imaging (e.g., assessing vascular calcification or pericoronary adipose tissue) to environmental science (e.g., measuring lichen coverage) and agricultural applications (e.g., quantifying pesticide coverage on leaves), ultimately enhancing research productivity and enabling more comprehensive analyses.