Sperm-cell DNA fragmentation prediction using label-free quantitative phase imaging and deep learning (Conference Presentation)

Lioz Noy; Itay Barnea; Simcha Mirsky; Dotan Kambar; Mattan Levi; Natan Tzvi Shaked

doi:10.1117/12.2635449

3 October 2022 Sperm-cell DNA fragmentation prediction using label-free quantitative phase imaging and deep learning (Conference Presentation)

Lioz Noy, Itay Barnea, Simcha Mirsky, Dotan Kambar, Mattan Levi, Natan Tzvi Shaked

Author Affiliations +

Proceedings Volume 12226, Applications of Digital Image Processing XLV; 122260W (2022) https://doi.org/10.1117/12.2635449
Event: SPIE Optical Engineering + Applications, 2022, San Diego, California, United States

Abstract

Intracytoplasmic sperm injection (ICSI) is the most common practice for in vitro fertilization (IVF) treatments. In ICSI, a single sperm is selected and injected into an oocyte. The quality of the sperm and specifically its DNA fragmentation index (DFI) have significant effects on the fertilization success rate. In our research, we use computer vision and deep learning methods to predict DFI scoring for a single sperm cell. Each cell in the dataset was acquired using multiple white light microscopy techniques combined with state-of-the-art interferometry. In our results, we see a strong correlation between the stained images and our score prediction which can be used in the ICSI process.

Conference Presentation

Citation Download Citation

Lioz Noy, Itay Barnea, Simcha Mirsky, Dotan Kambar, Mattan Levi, and Natan Tzvi Shaked "Sperm-cell DNA fragmentation prediction using label-free quantitative phase imaging and deep learning (Conference Presentation)", Proc. SPIE 12226, Applications of Digital Image Processing XLV, 122260W (3 October 2022); https://doi.org/10.1117/12.2635449

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KEYWORDS

Adaptive optics

Computer vision technology

Digital image correlation

Image processing

In vitro testing

Interferometry

Machine vision

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