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

Roy Melendez; Cesar Beltran Castanon; Rosario Medina-Rodriguez

doi:10.1109/CBMS52027.2021.00084

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

Roy Melendez
, Cesar Beltran Castanon
, Rosario Medina-Rodriguez

Sección de Ingeniería Informática

Pontificia Universidad Católica del Perú

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

7 Scopus citations

Abstract

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.

Original language	English
Title of host publication	Proceedings - 2021 IEEE 34th International Symposium on Computer-Based Medical Systems, CBMS 2021
Editors	Joao Rafael Almeida, Alejandro Rodriguez Gonzalez, Linlin Shen, Bridget Kane, Agma Traina, Paolo Soda, Jose Luis Oliveira
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	91-96
Number of pages	6
ISBN (Electronic)	9781665441216
DOIs	https://doi.org/10.1109/CBMS52027.2021.00084
State	Published - Jun 2021
Event	34th IEEE International Symposium on Computer-Based Medical Systems, CBMS 2021 - Virtual, Online Duration: 7 Jun 2021 → 9 Jun 2021

Publication series

Name	Proceedings - IEEE Symposium on Computer-Based Medical Systems
Volume	2021-June
ISSN (Print)	1063-7125

Conference

Conference	34th IEEE International Symposium on Computer-Based Medical Systems, CBMS 2021
City	Virtual, Online
Period	7/06/21 → 9/06/21

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

U-net architecture
deep learning
image segmentation
sperm cell micrographs

Access to Document

10.1109/CBMS52027.2021.00084

Cite this

Melendez, R., Castanon, C. B., & Medina-Rodriguez, R. (2021). Sperm cell segmentation in digital micrographs based on convolutional neural networks using U-net architecture. In J. R. Almeida, A. R. Gonzalez, L. Shen, B. Kane, A. Traina, P. Soda, & J. L. Oliveira (Eds.), Proceedings - 2021 IEEE 34th International Symposium on Computer-Based Medical Systems, CBMS 2021 (pp. 91-96). Article 9474693 (Proceedings - IEEE Symposium on Computer-Based Medical Systems; Vol. 2021-June). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CBMS52027.2021.00084

Melendez, Roy ; Castanon, Cesar Beltran ; Medina-Rodriguez, Rosario. / Sperm cell segmentation in digital micrographs based on convolutional neural networks using U-net architecture. Proceedings - 2021 IEEE 34th International Symposium on Computer-Based Medical Systems, CBMS 2021. editor / Joao Rafael Almeida ; Alejandro Rodriguez Gonzalez ; Linlin Shen ; Bridget Kane ; Agma Traina ; Paolo Soda ; Jose Luis Oliveira. Institute of Electrical and Electronics Engineers Inc., 2021. pp. 91-96 (Proceedings - IEEE Symposium on Computer-Based Medical Systems).

@inproceedings{4438ce3653d14b87ae5f6063bdaffafd,

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

abstract = "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.",

keywords = "U-net architecture, deep learning, image segmentation, sperm cell micrographs",

author = "Roy Melendez and Castanon, \{Cesar Beltran\} and Rosario Medina-Rodriguez",

note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 34th IEEE International Symposium on Computer-Based Medical Systems, CBMS 2021 ; Conference date: 07-06-2021 Through 09-06-2021",

year = "2021",

month = jun,

doi = "10.1109/CBMS52027.2021.00084",

language = "English",

series = "Proceedings - IEEE Symposium on Computer-Based Medical Systems",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "91--96",

editor = "Almeida, \{Joao Rafael\} and Gonzalez, \{Alejandro Rodriguez\} and Linlin Shen and Bridget Kane and Agma Traina and Paolo Soda and Oliveira, \{Jose Luis\}",

booktitle = "Proceedings - 2021 IEEE 34th International Symposium on Computer-Based Medical Systems, CBMS 2021",

}

Melendez, R, Castanon, CB & Medina-Rodriguez, R 2021, Sperm cell segmentation in digital micrographs based on convolutional neural networks using U-net architecture. in JR Almeida, AR Gonzalez, L Shen, B Kane, A Traina, P Soda & JL Oliveira (eds), Proceedings - 2021 IEEE 34th International Symposium on Computer-Based Medical Systems, CBMS 2021., 9474693, Proceedings - IEEE Symposium on Computer-Based Medical Systems, vol. 2021-June, Institute of Electrical and Electronics Engineers Inc., pp. 91-96, 34th IEEE International Symposium on Computer-Based Medical Systems, CBMS 2021, Virtual, Online, 7/06/21. https://doi.org/10.1109/CBMS52027.2021.00084

Sperm cell segmentation in digital micrographs based on convolutional neural networks using U-net architecture. / Melendez, Roy; Castanon, Cesar Beltran; Medina-Rodriguez, Rosario.
Proceedings - 2021 IEEE 34th International Symposium on Computer-Based Medical Systems, CBMS 2021. ed. / Joao Rafael Almeida; Alejandro Rodriguez Gonzalez; Linlin Shen; Bridget Kane; Agma Traina; Paolo Soda; Jose Luis Oliveira. Institute of Electrical and Electronics Engineers Inc., 2021. p. 91-96 9474693 (Proceedings - IEEE Symposium on Computer-Based Medical Systems; Vol. 2021-June).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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

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 - https://www.scopus.com/pages/publications/85110914186

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 -

Melendez R, Castanon CB, Medina-Rodriguez R. Sperm cell segmentation in digital micrographs based on convolutional neural networks using U-net architecture. In Almeida JR, Gonzalez AR, Shen L, Kane B, Traina A, Soda P, Oliveira JL, editors, Proceedings - 2021 IEEE 34th International Symposium on Computer-Based Medical Systems, CBMS 2021. Institute of Electrical and Electronics Engineers Inc. 2021. p. 91-96. 9474693. (Proceedings - IEEE Symposium on Computer-Based Medical Systems). doi: 10.1109/CBMS52027.2021.00084

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

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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