Sperm cell segmentation in digital micrographs based on convolutional neural networks using u-net architecture

• Main Author: Melendez Melendez, Roy Kelvin
• Other Authors: Beltrán Castañón, César Armando
• Format: Dissertation
• Language: English
• Published: Pontificia Universidad Católica del Perú 2021
• Subjects: Redes neuronales (Computación); Espermatozoides > Análisis; http://purl.org/pe-repo/ocde/ford#1.02.01
• Online Access: http://hdl.handle.net/20.500.12404/19908

Human infertility is considered a serious disease of the the reproductive system that affects more than 10% of couples worldwide,and more than 30% of reported cases are related to men. The crucial step in evaluating male in fertility is a semen analysis, highly dependent on sperm morphology. However,this analysis is done at the laboratory manually and depends mainly on the doctor’s experience. Besides,it is laborious, and there is also a high degree of interlaboratory variability in the results. This article proposes applying a specialized convolutional neural network architecture (U-Net),which focuses on the segmentation of sperm cells in micrographs to overcome these problems.The results showed high scores for the model segmentation metrics such as precisión (93%), IoU score (86%),and DICE score of 93%. Moreover,we can conclude that U-net architecture turned out to be a good option to carry out the segmentation of sperm cells.