Author:
Kovalev V.Y., ,Shishkin A.G.,
Abstract
In this paper, a multiclass image semantic segmentation problem was solved. For analysis, images of the intracytoplasmic sperm injection process were used. For training the neural network, 656 frames were manually labelled. As a result, each pixel of the images was assigned to one of four classes: microinjector, suction micropipette, oolemma, background. An analysis of modern approaches was carried out and the best architecture, encoders, and hyperparameters of the neural network were selected experimentally: the convolutional neural network FPN (feature pyramid network) with the resnext101 encoder having a depth of 101 layers with 32 parallel separable convolutions. The developed neural network model has allowed obtaining the segmentation efficiency of IOU=0.96 at the algorithm speed of 15 frames per second.
Publisher
Samara National Research University
Subject
Electrical and Electronic Engineering,Computer Science Applications,Atomic and Molecular Physics, and Optics
Reference26 articles.
1. Murid J, Essam M. Intracytoplasmic sperm injection – factors affecting fertilization. In Book: Darwish AMM, ed. Enhancing success of assisted reproduction. Rijeka: IntechOpen; 2012: 117-144.
2. Hajiyavand AM, Saadat M, Abena A, Sadak F, Sun X. Effect of injection speed on oocyte deformation in ICSI. micromachines 2019; 10: 226.
3. Hafiz P, Nematollahi M, Boostani R, Jahromi BN. Predicting implantation outcome of in vitro fertilization and intracytoplasmic sperm injection using data mining techniques. Int J Fertil Steril 2017; 11(3): 184-190.
4. Mostaar A, Sattari MR, Hosseini S, Deevband MR. Use of artificial neural networks and PCA to predict results of infertility treatment in the ICSI method. J Biomed Phys Eng 2019; 9(6): 679-686.
5. Rubino P, Viganò P, Luddi A, Piomboni P. The ICSI procedure from past to future: a systematic review of the more controversial aspects. Hum Reprod Update 2015; 22(2): 194-227.