Automatic evaluation of human oocyte developmental potential from microscopy images

• URL: http://arxiv.org/abs/2103.00302v1
• Date: Sat, 27 Feb 2021 19:36:04 GMT
• Title: Automatic evaluation of human oocyte developmental potential from microscopy images
• Authors: Denis Baru\v{c}i\'c (1), Jan Kybic (1), Olga Tepl\'a (2), Zinovij Topurko (2), Irena Kratochv\'ilov\'a (3) ((1) Czech Technical University in Prague, Czech Republic, (2) The First Faculty of Medicine and General Teaching Hospital, Czech Republic, (3) Institute of Physics of the Czech Academy of Sciences, Czech Republic)
• Abstract summary: We propose an automatic system to improve the speed, repeatability, and accuracy of this process. We first localize individual oocytes and identify their principal components using CNN (U-Net) segmentation. The presented approach leads to the classification accuracy of 70%.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Infertility is becoming an issue for an increasing number of couples. The most common solution, in vitro fertilization, requires embryologists to carefully examine light microscopy images of human oocytes to determine their developmental potential. We propose an automatic system to improve the speed, repeatability, and accuracy of this process. We first localize individual oocytes and identify their principal components using CNN (U-Net) segmentation. We calculate several descriptors based on geometry and texture. The final step is an SVM classifier. Both the segmentation and classification training are based on expert annotations. The presented approach leads to the classification accuracy of 70%.

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