Probing spermiogenesis: A digital strategy for mouse acrosome classification

Alessandro Taloni, Francesc Font-Clos, Luca Guidetti, Simone Milan, Miriam Ascagni, Chiara Vasco, Maria Enrica Pasini, Maria Rosa Gioria, Emilio Ciusani, Stefano Zapperi, Caterina A.M. La Porta

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Abstract

Classification of morphological features in biological samples is usually performed by a trained eye but the increasing amount of available digital images calls for semi-automatic classification techniques. Here we explore this possibility in the context of acrosome morphological analysis during spermiogenesis. Our method combines feature extraction from three dimensional reconstruction of confocal images with principal component analysis and machine learning. The method could be particularly useful in cases where the amount of data does not allow for a direct inspection by trained eye.

Original languageEnglish
Article number3748
JournalScientific Reports
Volume7
Issue number1
DOIs
Publication statusPublished - Dec 1 2017

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    Taloni, A., Font-Clos, F., Guidetti, L., Milan, S., Ascagni, M., Vasco, C., Pasini, M. E., Gioria, M. R., Ciusani, E., Zapperi, S., & La Porta, C. A. M. (2017). Probing spermiogenesis: A digital strategy for mouse acrosome classification. Scientific Reports, 7(1), [3748]. https://doi.org/10.1038/s41598-017-03867-7