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1.
Sci Rep ; 8(1): 17060, 2018 Nov 14.
Article in English | MEDLINE | ID: mdl-30425302

ABSTRACT

A correction to this article has been published and is linked from the HTML and PDF versions of this paper. The error has not been fixed in the paper.

2.
Sci Rep ; 7(1): 3748, 2017 06 16.
Article in English | MEDLINE | ID: mdl-28623263

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.


Subject(s)
Acrosome , Image Processing, Computer-Assisted/methods , Spermatogenesis/physiology , Animals , Male , Mice , Microscopy, Confocal/methods
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