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11 March 2020 High sensitivity SLIM imaging and deep learning to correlate sperm morphology and fertility outcomes (Conference Presentation)
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Proceedings Volume 11249, Quantitative Phase Imaging VI; 112490C (2020) https://doi.org/10.1117/12.2550470
Event: SPIE BiOS, 2020, San Francisco, California, United States
Abstract
Fluorescence microscopy has been proven a valid method of classifying sperm with different characteristics such as gender. However, it has been observed that they introduced an increase in oxidative stress as well as undesired bias. We show that spatial light interference microscopy, a QPI method that can reveal the intrinsic contrast of cell structures, is ideal for the study of sperm. To enable high-throughput sperm quality assessment using QPI, we propose a new analysis method based on deep learning and the U-Net architecture. We show that our model can achieve satisfying precision and accuracy and that it can be integrated within our image acquisition software for near real-time analysis.
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Mikhail E. Kandel, Yuchen R. He, Marcello Rubessa, Matthew B. Wheeler, and Gabriel Popescu "High sensitivity SLIM imaging and deep learning to correlate sperm morphology and fertility outcomes (Conference Presentation)", Proc. SPIE 11249, Quantitative Phase Imaging VI, 112490C (11 March 2020); https://doi.org/10.1117/12.2550470
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