Reproductive outcomes predicted by phase imaging with computational specificity of spermatozoon ultrastructure.

Proceedings of the National Academy of Sciences of the United States of America
Mikhail E KandelGabriel Popescu

Abstract

The ability to evaluate sperm at the microscopic level, at high-throughput, would be useful for assisted reproductive technologies (ARTs), as it can allow specific selection of sperm cells for in vitro fertilization (IVF). The tradeoff between intrinsic imaging and external contrast agents is particularly acute in reproductive medicine. The use of fluorescence labels has enabled new cell-sorting strategies and given new insights into developmental biology. Nevertheless, using extrinsic contrast agents is often too invasive for routine clinical operation. Raising questions about cell viability, especially for single-cell selection, clinicians prefer intrinsic contrast in the form of phase-contrast, differential-interference contrast, or Hoffman modulation contrast. While such instruments are nondestructive, the resulting image suffers from a lack of specificity. In this work, we provide a template to circumvent the tradeoff between cell viability and specificity by combining high-sensitivity phase imaging with deep learning. In order to introduce specificity to label-free images, we trained a deep-convolutional neural network to perform semantic segmentation on quantitative phase maps. This approach, a form of phase imaging with...Continue Reading

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Citations

Dec 31, 2020·Sensors·Viktorija ValiuškaitėTomas Krilavičius
May 21, 2021·Lasers in Surgery and Medicine·Lei TianNicholas J Durr
Sep 4, 2021·Light, Science & Applications·Gabriel Popescu

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Software Mentioned

Keras
Net
IMSI
QPI
PICS
ImageJ
Tensorflow
MATLAB
SLIM
Image Segmented App

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