یک راهکار نیمه‌نظارتی جدید برای انتخاب اسپرم مناسب در درمان ناباروری
محورهای موضوعی : پردازش چند رسانه ای، سیستمهای ارتباطی، سیستمهای هوشمند
آصفه توکلی پله شاهی
1
,
سیدابوالقاسم میرروشندل
2
,
فاطمه قاسمیان
3
1 - دانشجوی کارشناسی ارشد / دانشگاه گیلان
2 - عضو هیات علمی / گروه مهندسی کامپیوتر، دانشکده فنی، دانشگاه گیلان
3 - عضور هیات علمی / گروه زیست شناسی، دانشکده علوم، دانشگاه گیلان
کلید واژه: یادگیری عمیق نیمه نظارتی, ناباروری, آنالیز خودکار تصاویر, مورفولوژی اسپرم انسان,
چکیده مقاله :
: امروزه، رشد چشمگیر ناباروری در جوامع مختلف و نیاز به بررسی هر یک از عوامل ناشی از ناتوانی مردان و زنان در ایجاد و تشدید آن بر کسی پوشیده نیست. در این میان، آمارهای سازمان بهداشت جهانی از رشد سریع عوامل ناتوانی مردانه در باروری تا حدود 50 درصد حکایت دارد؛ که نشان‌دهنده اهمیت بالای تجزیه و تحلیل مورفولوژیکی اسپرم، به عنوان یکی از مهم‌ترین و اساسی‌ترین گام‌ها در تجزیه و تحلیل مایع منی، به منظور اجرای لقاح مصنوعی است. در این مقاله، تلاش شده‌است تا به کمک یکی از روش‌های یادگیری نیمه‌نظارتی، موسوم به شبکه نردبانی، به استخراج ویژگی‌های بخش‌های مختلف اسپرم (مانند سر، واکوئل و آکروزوم) پرداخته و در ادامه با طبقه‌بندی آن‌ها در دو گروه اسپرم‌های طبیعی و غیرطبیعی، به انتخاب اسپرم مناسب، به منظور شرکت در فرآیند لقاح مصنوعی موفق شویم. پژوهش حاضر با اعمال تغییرات و بهبود عوامل مختلف به‌ویژه نویز ورودی، نتایج مناسبی را در آنالیز تصاویر با وضوح پایین و بدون رنگ‌آمیزی کسب کرده است. بررسی مدل پیشنهادی برای هر سه بخش اسپرم (سر، واکوئل و آکروزوم) موفق شد با وجود تصاویری با کیفیت پایین، نتایج چشمگیر بیش از 70% را برای سر و آکروزوم و بیش از 80% را برای واکوئل به‌دست آورد.
Introduction:Nowadays infertility is recognized as one of the most common clinical problems around the globe, and one of the most worrying social issues in different cultures and societies. In the meantime, efforts have always been made to prevent the progression of infertility caused by this factor by carefully examining the most effective male factor - as one of the potential parties in infertility problems - that is, analyzing the quantity and quality of sperm. On one hand, traditional methods have lots of problems such as inadequate accuracy, clinicians' disagreements, and prolonged treatment. On the other hand, the successes of machine learning in many areas prompted researchers to move toward automating sperm morphology analysis by means of machine learning.Methods:The ladder network as a semi-supervised learning algorithm, by using a small number of labeled samples and a larger part of unlabeled data, shows suitability and compliance with the real-world requirements in this field of study. In this regard, in order to implement ladder networks, the structure of stack noise removal auto-encoders with the architecture of two parallel encoders has been used to represent the samples and a decoder to reconstruct the samples. The present study by applying changes and improving various factors, especially input noise, has obtained good results in the analysis of low-resolution images without coloring.Results:The proposed model succeeded by extracting positive and fruitful features from the images of the head, acrosome, and vacuole of human sperm, showing an acceptable accuracy for classifying them into two natural and abnormal classes, and finally selecting the appropriate sperm to participate in the artificial insemination process. The study of the proposed model for all three sperm sections (head, vacuole, and acrosome) succeeded, despite low-quality images, achieving impressive results of more than 70% for the head and acrosome and more than 80% for the vacuole.Conclusion: In the future, we intend to improve the proposed model by finding ways to increase the accuracy and reduce the error of the test results and show that the change in the type of noise or how it is applied to the network will have a significant impact on the network performance.
|
[1] |
Sepidarkish, M., Almasi-Hashiani, A., Shokri, F., Vesali, S.,Karimi, E., and Omani, R., “Prevalence of Infertility Problems among Iranian Infertile Patients Referred to Royan Institute”, International Journal of Fertility and Sterility, Vol. 10, No. 3, pp. 278-282, Oct-Dec 2016. |
|
[2] |
Sun, H., Gong, T., T., Jiang, Y., T., Zhang, S., Zhao, Y. H., and Wu, Q., J., “Global, regional, and national prevalence and disability-adjusted life-years for infertility in 195 countries and territories, 1990–2017: results from a global burden of disease study, 2017”, AGING, Vol. 11, No. 23, Dec 2019. |
|
[3] |
Parsanezhad, M., E., Namvar, B., Zare, N., Keramati, P., Khalili, A., and Parsa-Nezhad, M., “Epidemiology and Etiology of Infertility in Iran, Systematic Review and Meta-Analysis”, Journal of Women’s Health, Issues & Care, Vol. 2, Issue 6, October 2013. |
|
[4] |
Abrao, M. S., Muzii, L., Marana, R., “Anatomical causes of female infertility and their management”, International Journal of Gynecology and Obstetrics, Vol. 123, pp. S18-S24, Dec 2013. |
|
[5] |
Inhorn, M., C., Patrizio, P., “Infertility around the globe: new thinking on gender, reproductive technologies and global movements in the 21st century”, Human Reproduction Update, Vol. 21, No. 4, pp. 411 – 426, March 2015. |
|
[6] |
Girela, J. L., Gil, D., Johnsson, M., Gomez-Torres, M. J., and Juan, J. D., “Semen Parameters Can Be Predicted from Environmental Factors and Lifestyle Using Artificial Intelligence Methods”, BIOLOGY OF REPRODUCTION, Vol. 88, Issue 4, Apr 2013. |
|
[7] |
Abbirami, V. S., Shanthi, V., “Spermatozoa Segmentation and Morphological Parameter Analysis Based Detection of Teratozoospermia”, International Journal of Computer Applications, Vol. 3, No.7, June 2010. |
|
[8] |
Palermo, G., Joris, H., Devroey, P., Van Steirteghem, A. C., “Pregnancies after intracytoplasmic injection of single spermatozoon into an oocyte”, The Lancet, Vol. 340, July 1992. |
|
[9] |
Monte, G. L., Murisier, F., Piva, I., Germond, M., Marci, R., “Focus on intracytoplasmic morphologically selected sperm injection (IMSI): a mini-review”,Asian journal of andrology, 2013. |
|
[10] |
Bijar, A., Benavent, A. P., Mikaeili, M., Khayati, R., “Fully automatic identification and discrimination of sperm’s parts in microscopic images of stained human semen smear”, Journal of Biomedical Science and Engineering, Vol. 5, No.7, pp. 384-395, July 2012. |
|
[11] |
Sanchez, L., Petkov, N., Alegra, E., “Statistical approach to boar semen evaluation using intracellular intensity distribution of head images”, Cellular and molecular biology, Vol. 52, Issue 6, pp. 38-43, 2006. |
|
[12] |
Alegra, E., Biehl, M., Petkov, N., and Sanchez, L., “Automatic classification of the acrosome status of boar spermatozoa using digital image processing and LVQ”, Computers in Biology and Medicine, Vol. 38, Issue 4, pp. 461-468, Apr 2008. |
|
[13] |
Vicente-Fiel, S., Palacin, I., Santolaria, P., Yaniz, J. L., “A comparative study of sperm morphometric subpopulations in cattle, goat, sheep and pigs using a computer-assisted fluorescence method (CASMA-F)”, Animal Reproduction Science, Vol. 139, pp.182-189, Apr 2013. |
|
[14] |
Uyar, A., Bener, A., Nadir Ciray, H., “Predictive Modeling of Implantation Outcome in an In Vitro Fertilization Setting: An Application of Machine Learning Methods”, Medical Decision Making, Vol. 35, Issue 6, pp. 714-725, May 2015. |
|
[15] |
Chang, V., Saavedra, J. M., Castaneda, V., Sarabia, L., Hitschfeld, N. Hartel, S., “Gold-standard and improved framework for spermhead segmentation”, Computer Methods and Programs in Biomedicine, Vol. 117, Issue 2, pp. 225-237 Nov. 2014. |
|
[16] |
Chang, V., Heutte, L., Petitjean, Hartel, S., Hitschfeld, N., “Automatic classification of human sperm head morphology”, Computers in Biology and Medicine, Vol. 84, pp. 205-216, May 2017. |
|
[17] |
Mendoza, F., Manotas, A. K. D. L. H., Ariza, P., Ojeda, J. A. S., Melo, M. P., “Fertility Analysis Method Based on Supervised and Unsupervised Data Mining Techniques”, International Journal of Applied Engineering Research, Vol. 11, No 21, pp. 10374-10379, Nov 2016. |
|
[18] |
Javadi, S., Mirroshandel, S. A., “A novel deep learning method for automatic assessment of human sperm images”, Computers in Biology and Medicine, Vol. 109, pp. 182–194, 2019. |
|
[19] |
Riordon, J., Callum, C. M., Sinton, D., “Deep learning for the classification of human sperm”, Computers in Biology and Medicine, Vol.111, Aug. 2019. |
|
[20] |
Iqbal, I., Mustafa, Gh., Ma, J., “Deep Learning-Based Morphological Classification of Human Sperm Heads”, Diagnostics, Vol. 10, Issue 5, May 2020. |
|
[21] |
Pezeshki, M., Fan, L., Brakel, P., Courville, A., Bengio, Y., “Deconstructing the Ladder Network Architecture”, Proceedings of the 33 rd International Conference on Machine Learning, New York, NY, USA, Vol. 48, 2016. |
|
[22] |
Rasmus, A., Valpola, H., Honkala, M., Berglund, M., Raiko, T., “Semi-Supervised Learning with Ladder Networks”, Proceedings of the 28th International Conference on Neural Information Processing Systems, Vol. 2, pp. 3546–3554, Neural and Evolutionary Computing (cs.NE), Nov 2015 |
|
[23] |
Ghasemian, F., Mirroshandel, S. A., Monji-Azad, S., Azarnia, M., Zahiri, Z., “An efficient method for automatic morphological abnormality detection from human sperm images”, Comput Methods Programs Biomed, Vol. 122, pp. 409–420, Dec. 2015. |
_||_
