Fatemeh Ghasemian1, Seyed Abolghasem Mirroshandel2, Sara Monji-Azad3, Mahnaz Azarnia1, Ziba Zahiri4. 1. Department of Biology, University of Kharazmi, Tehran, Iran. 2. Department of Computer Engineering, University of Guilan, Rasht, Iran. Electronic address: mirroshandel@guilan.ac.ir. 3. Department of Computer Engineering, University of Guilan, Rasht, Iran. 4. Infertility Therapy Center (IVF), Alzahra Educational and Remedial Center, Guilan, Iran.
Abstract
BACKGROUND AND OBJECTIVE: Sperm morphology analysis (SMA) is an important factor in the diagnosis of human male infertility. This study presents an automatic algorithm for sperm morphology analysis (to detect malformation) using images of human sperm cells. METHODS: The SMA method was used to detect and analyze different parts of the human sperm. First of all, SMA removes the image noises and enhances the contrast of the image to a great extent. Then it recognizes the different parts of sperm (e.g., head, tail) and analyzes the size and shape of each part. Finally, the algorithm classifies each sperm as normal or abnormal. Malformations in the head, midpiece, and tail of a sperm, can be detected by the SMA method. In contrast to other similar methods, the SMA method can work with low resolution and non-stained images. Furthermore, an image collection created for the SMA, has also been described in this study. This benchmark consists of 1457 sperm images from 235 patients, and is known as human sperm morphology analysis dataset (HSMA-DS). RESULTS: The proposed algorithm was tested on HSMA-DS. The experimental results show the high ability of SMA to detect morphological deformities from sperm images. In this study, the SMA algorithm produced above 90% accuracy in sperm abnormality detection task. Another advantage of the proposed method is its low computation time (that is, less than 9s), as such, the expert can quickly decide to choose the analyzed sperm or select another one. CONCLUSIONS: Automatic and fast analysis of human sperm morphology can be useful during intracytoplasmic sperm injection for helping embryologists to select the best sperm in real time.
BACKGROUND AND OBJECTIVE: Sperm morphology analysis (SMA) is an important factor in the diagnosis of humanmale infertility. This study presents an automatic algorithm for sperm morphology analysis (to detect malformation) using images of human sperm cells. METHODS: The SMA method was used to detect and analyze different parts of the human sperm. First of all, SMA removes the image noises and enhances the contrast of the image to a great extent. Then it recognizes the different parts of sperm (e.g., head, tail) and analyzes the size and shape of each part. Finally, the algorithm classifies each sperm as normal or abnormal. Malformations in the head, midpiece, and tail of a sperm, can be detected by the SMA method. In contrast to other similar methods, the SMA method can work with low resolution and non-stained images. Furthermore, an image collection created for the SMA, has also been described in this study. This benchmark consists of 1457 sperm images from 235 patients, and is known as human sperm morphology analysis dataset (HSMA-DS). RESULTS: The proposed algorithm was tested on HSMA-DS. The experimental results show the high ability of SMA to detect morphological deformities from sperm images. In this study, the SMA algorithm produced above 90% accuracy in sperm abnormality detection task. Another advantage of the proposed method is its low computation time (that is, less than 9s), as such, the expert can quickly decide to choose the analyzed sperm or select another one. CONCLUSIONS: Automatic and fast analysis of human sperm morphology can be useful during intracytoplasmic sperm injection for helping embryologists to select the best sperm in real time.
Authors: Christopher McCallum; Jason Riordon; Yihe Wang; Tian Kong; Jae Bem You; Scott Sanner; Alexander Lagunov; Thomas G Hannam; Keith Jarvi; David Sinton Journal: Commun Biol Date: 2019-07-03
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Authors: Alejandro Maroto-Morales; Olga García-Álvarez; Manuel Ramón; Felipe Martínez-Pastor; M Rocío Fernández-Santos; A Josefa Soler; José Julián Garde Journal: Asian J Androl Date: 2016 Nov-Dec Impact factor: 3.285