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

• Autor: Melendez Melendez, Roy Kelvin
• Formato: tesis de maestría
• Fecha de Publicación: 2021
• Institución: Pontificia Universidad Católica del Perú
• Repositorio: PUCP-Tesis
• Lenguaje: inglés
• OAI Identifier: oai:tesis.pucp.edu.pe:20.500.12404/19908
• Enlace del recurso: http://hdl.handle.net/20.500.12404/19908
• Nivel de acceso: acceso abierto
• Materia: Redes neuronales (Computación); Espermatozoides--Análisis; https://purl.org/pe-repo/ocde/ford#1.02.01

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.