Sperm segmentation and abnormalities detection during the ICSI procedure using machine learning algorithms

(1) About 15-20% of couples struggle with the problem of infertility. 30 to 40% of these cases are caused by abnormalities in the structure and motility of sperm. Sometimes the only possibility for such people is to use the procedure of artificial insemination. CASA systems are used to increase the efficiency of this procedure by selecting the appropriate sperm cell. (2) This paper presents an approach to the sperm classification on the basis of its entire structure analysis, including flagella - often poorly visible and therefore ignored in the CASA systems element. The training of the Mask R-CNN architecture was performed on 2 publicly available and one specially created for this purpose sperm database. A 14-element feature vector was also proposed for the classification of 4 classes of typical head defects (amorphous, normal, tapered and pyriform) by the Support Vector Machine. (3) The sperm head (mAP 94.28%) and the whole flagellum (mAP 90.29%) were successfully detected. However, the flagella segmentation results were significantly lower (50.88%) than that the head segmentation (88.32%). Classification with SVM scored 82% accuracy. (4) Research has shown that segmentation and the use of a simple SVM classifier allow for quite good results in the classification of sperm defects. However, it is important to develop a larger whole sperm database, to improve the segmentation results.