Probing spermiogenesis: a digital strategy for mouse acrosome classification

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