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

Roy Meléndez, C. Beltran Castañon, Rosario Medina-Rodriguez

Producción científica: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva

2 Citas (Scopus)


Human infertility is considered a serious disease of 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 infertility 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 precision (93%), IoU score (88%), and DICE score of 94%. Moreover, we can conclude that U-net architecture turned out to be a good option to carry out the segmentation of sperm cells.
Idioma originalEspañol
Título de la publicación alojadaProceedings - IEEE Symposium on Computer-Based Medical Systems
Número de páginas6
EstadoPublicada - 1 jun. 2021
Publicado de forma externa

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