Bouadi, O.; Yao, C.; Zeng, J.; Beason, D.; Inda, N.; Malone, Z.; Yoshihara, J.; Manjally, A. V.; Johnson, C.; Cherry, J.; Chen, C.-Y.; Huang, T.-C.; Popovic, B.; Henley, M.; Liu, G.; Kharitonova, E.; Zeldich, E.; Aichelman, H.; Davies, S. W.; Walentek, P.; Tian, Y.; Man, H.; Ozsen, E.; Harder, K.; Gilmore, T. D.; Pitt, D.; Tay, T. L.

Running on the highly popular and accessible ImageJ/Fiji platform for biological image analysis, we have established AutoMorFi as a streamlined interface for automated whole-image morphometric analysis that generates at least 47 measurements per cell or object in under 1 minute. We performed multiple validated cluster analyses on nonredundant morphometric parameters derived from AutoMorFi for various cell types, objects and organisms. We used images of rodent macrophages, human postmortem brain tissues from multiple sclerosis (MS) and Alzheimer\'s disease (AD) patients, iPSC/animal models for Down\'s syndrome and autism spectrum disorder (ASD), and organisms such as sea anemone and corals. AutoMorFi\'s adaptability extends across diverse imaging modalities including brightfield, confocal, or widefield fluorescence microscopy as well as underwater photography. Due to its ability for unlimited and unbiased sampling across any given image and high potential for modification and customization, AutoMorFi represents a transformative tool that will accelerate morphometric analysis and offer broad relevance in biological studies.