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

Significance The high incidence of human male factor infertility suggests a need for examining new ways of evaluating sperm cells. We present an approach that combines label-free imaging and artificial intelligence to obtain nondestructive markers for reproductive outcomes. Our phase-imaging system reveals nanoscale morphological details from unlabeled cells. Deep learning, on the other hand, provides a structural specificity map segmenting with high accuracy the head, midpiece, and tail. Using these binary masks applied to the quantitative phase images, we measure precisely the dry-mass content of each component. Remarkably, we found that the dry-mass ratios represent intrinsic markers with predictive power for zygote cleavage and blastocyst development.
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 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.