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Open AccessArticle

Oocytes Polar Body Detection for Automatic Enucleation

by Di Chen 1,2, Mingzhu Sun 1,2,* and Xin Zhao 1,2
1
Institute of Robotics and Automatic Information System (IRAIS), Nankai University, No. 94 Weijin Road, Nankai District, Tianjin 300000, China
2
Tianjin Key Laboratory of Intelligent Robotics (TJKLIR), Nankai University, No. 94 Weijin Road, Nankai District, Tianjin 300000, China
*
Author to whom correspondence should be addressed.
Academic Editors: Toshio Fukuda, Mohd Ridzuan bin Ahmad and Yajing Shen
Micromachines 2016, 7(2), 27; https://doi.org/10.3390/mi7020027
Received: 29 October 2015 / Revised: 30 January 2016 / Accepted: 4 February 2016 / Published: 14 February 2016
(This article belongs to the Special Issue Micro/Nano Robotics)
Enucleation is a crucial step in cloning. In order to achieve automatic blind enucleation, we should detect the polar body of the oocyte automatically. The conventional polar body detection approaches have low success rate or low efficiency. We propose a polar body detection method based on machine learning in this paper. On one hand, the improved Histogram of Oriented Gradient (HOG) algorithm is employed to extract features of polar body images, which will increase success rate. On the other hand, a position prediction method is put forward to narrow the search range of polar body, which will improve efficiency. Experiment results show that the success rate is 96% for various types of polar bodies. Furthermore, the method is applied to an enucleation experiment and improves the degree of automatic enucleation. View Full-Text
Keywords: micromanipulation; oocyte polar body detection; machine learning; polar body prediction micromanipulation; oocyte polar body detection; machine learning; polar body prediction
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MDPI and ACS Style

Chen, D.; Sun, M.; Zhao, X. Oocytes Polar Body Detection for Automatic Enucleation. Micromachines 2016, 7, 27.

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