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.

Probing spermiogenesis : a digital strategy for mouse acrosome classification / A. Taloni, F. Font Clos, L. Guidetti, S. Milan, M. Ascagni, C. Vasco, M.E. Pasini, M. Gioria, E. Ciusani, S. Zapperi, C.A.M. La Porta. - In: SCIENTIFIC REPORTS. - ISSN 2045-2322. - 7:1(2017 Jun 16). [10.1038/s41598-017-03867-7]

Probing spermiogenesis : a digital strategy for mouse acrosome classification

A. Taloni
Primo
;
C. Vasco;M.E. Pasini;M. Gioria;S. Zapperi
Penultimo
;
C.A.M. La Porta
2017-06-16

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.
Settore BIO/06 - Anatomia Comparata e Citologia
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/2434/505201
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