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. 2016 Feb 14;7(2):27.
doi: 10.3390/mi7020027.

Oocytes Polar Body Detection for Automatic Enucleation

Di Chen  1   2 , Mingzhu Sun  3   4 , Xin Zhao  5   6
Affiliations

Oocytes Polar Body Detection for Automatic Enucleation

Di Chen et al. Micromachines (Basel). .

Abstract

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.

Keywords: machine learning; micromanipulation; oocyte polar body detection; polar body prediction.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Polar body microscopic image. (a) The polar body of a mouse oocyte; (b–d) The polar bodies of porcine oocytes.
Figure 2
Figure 2
The process of polar body detection.
Figure 3
Figure 3
Local images and their gradient histograms in polar body and non-polar body image patches. (a) Image and gradient histogram around a non-polar body image patch; (b) Image and gradient histogram around a polar body image patch; (c) Image and gradient histogram in the center of a non-polar body image patch; (d) Image and gradient histogram in the center of a polar body image patch.
Figure 4
Figure 4
Original Histogram of Oriented Gradient (HOG) algorithm and improved HOG algorithm. (a) Original HOG algorithm; (b) Weights of blocks in the original HOG algorithm; (c) Weights of blocks in the improved HOG algorithm.
Figure 5
Figure 5
Schematic diagram of cosine algorithm.
Figure 6
Figure 6
The sample library. (a) Polar body-Positive samples; (b) Polar body-Negative samples.
Figure 7
Figure 7
Polar body position prediction result. (a) The prediction result; (b) The cosine value.
Figure 8
Figure 8
Prediction result. (a) Microscopic image; (b) The oocyte area; (c) Edge detection result; (d) The contour of the oocyte; (e) The prediction result.
Figure 9
Figure 9
Detection results for various oocytes.
Figure 10
Figure 10
Detection results of oocyte rotation.
Figure 11
Figure 11
Detection results of the enucleation process.
See this image and copyright information in PMC

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