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Automatic segmentation of intracytoplasmic sperm injection images
V.Y. Kovalev 1, A.G. Shishkin 1

Lomonosov Moscow State University, 119234, Moscow, Russia, Leninskie Gory st., 1

 PDF, 987 kB

DOI: 10.18287/2412-6179-CO-1060

Pages: 628-633.

Full text of article: Russian language.

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.

intracytoplasmic sperm injection, semantic segmentation, convolutional neural networks.

Kovalev VY, Shishkin AG. Automatic segmentation of intracytoplasmic sperm injection images. Computer Optics 2022; 46(4): 628-633. DOI: 10.18287/2412-6179-CO-1060.


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