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

Mikhail E. Kandel, Marcello Rubessa, Yuchen R. He, Sierra Schreiber, Sasha Meyers, Luciana Matter Naves, Molly K. Sermersheim, G. Scott Sell, Michael J. Szewczyk, Nahil Sobh, Matthew B. Wheeler, Gabriel Popescu

Research output: Contribution to journalArticlepeer-review


The ability to evaluate sperm at the microscopic level, at highthroughput, 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 cellsorting 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 highsensitivity 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 computational specificity (PICS), allowed us to efficiently analyze thousands of sperm cells and identify correlations between dry-mass content and artificial-reproduction outcomes. Specifically, we found that the dry-mass content ratios between the head, midpiece, and tail of the cells can predict the percentages of success for zygote cleavage and embryo blastocyst formation.

Original languageEnglish (US)
Pages (from-to)18302-18309
Number of pages8
JournalProceedings of the National Academy of Sciences of the United States of America
Issue number31
StatePublished - Aug 4 2020


  • Assisted reproduction
  • Machine learning
  • Phase imaging with computational specificity
  • Quantitative phase imaging
  • Sperm

ASJC Scopus subject areas

  • General


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