TY - GEN
T1 - Sperm cell segmentation in digital micrographs based on convolutional neural networks using U-net architecture
AU - Melendez, Roy
AU - Castanon, Cesar Beltran
AU - Medina-Rodriguez, Rosario
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021/6
Y1 - 2021/6
N2 - 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.
AB - 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.
KW - U-net architecture
KW - deep learning
KW - image segmentation
KW - sperm cell micrographs
UR - http://www.scopus.com/inward/record.url?scp=85110914186&partnerID=8YFLogxK
U2 - 10.1109/CBMS52027.2021.00084
DO - 10.1109/CBMS52027.2021.00084
M3 - Conference contribution
AN - SCOPUS:85110914186
T3 - Proceedings - IEEE Symposium on Computer-Based Medical Systems
SP - 91
EP - 96
BT - Proceedings - 2021 IEEE 34th International Symposium on Computer-Based Medical Systems, CBMS 2021
A2 - Almeida, Joao Rafael
A2 - Gonzalez, Alejandro Rodriguez
A2 - Shen, Linlin
A2 - Kane, Bridget
A2 - Traina, Agma
A2 - Soda, Paolo
A2 - Oliveira, Jose Luis
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 34th IEEE International Symposium on Computer-Based Medical Systems, CBMS 2021
Y2 - 7 June 2021 through 9 June 2021
ER -
