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Proceedings Paper

High sensitivity SLIM imaging and deep learning to correlate sperm morphology and fertility outcomes (Conference Presentation)
Author(s): Mikhail E. Kandel; Yuchen R. He; Marcello Rubessa; Matthew B. Wheeler; Gabriel Popescu

Paper 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.

Paper Details

Date Published: 11 March 2020
Proc. SPIE 11249, Quantitative Phase Imaging VI, 112490C (11 March 2020); doi: 10.1117/12.2550470
Show Author Affiliations
Mikhail E. Kandel, Univ. of Illinois (United States)
Yuchen R. He, Univ. of Illinois (United States)
Marcello Rubessa, Univ. of Illinois (United States)
Matthew B. Wheeler, Univ. of Illinois (United States)
Gabriel Popescu, Univ. of Illinois (United States)

Published in SPIE Proceedings Vol. 11249:
Quantitative Phase Imaging VI
Yang Liu; Gabriel Popescu; YongKeun Park, Editor(s)

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