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

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 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.

Keywords: assisted reproduction; machine learning; phase imaging with computational specificity; quantitative phase imaging; sperm.

Fig. 1.

Experiment design and SLIM. (A) To find the relationship between dry-mass and assisted-reproduction outcomes, semen samples from the same ejaculate were used for embryo transfer and quantitative phase imaging. For each animal, a fraction of the embryos underwent zygote cleavage (fertilization), from which a smaller fraction formed blastocysts. To assay cellular ultrastructure, we performed high-resolution quantitative phase imaging and developed a deep-convolutional neural network to perform automatic annotation. The results of this network were used to relate the morphology to the aforementioned outcomes. (B) The SLIM system upgrades a conventional phase-contrast microscope with the ability to measure optical path-length shifts. Compared with typical phase contrast, SLIM highlights the mitochondria-rich midpiece as a denser portion of the cell (white arrow).

Fig. 2.

SLIM can image sperm as a fully automated slide scanner, with thousands of samples on each slide. (A) A large number of samples in each slide motivates the use of automated segmentation techniques. (B) The superior sensitivity of SLIM images is, in part, due to the use of spatially and temporally broadband fields. The partially coherent illumination corrupts the low frequencies, evident as a halo glow surrounding the cell. The halos are corrected by solving a nonlinear inverse problem. (C) Tomographic rendering of a spermatozoon using SLIM. The mitochondria-rich midpiece appear as substantially higher in dry-mass density. Rendering of the tomogram was performed using Amira (Thermo Fisher) with the “physics” color map corresponding to high phase values and a grayscale color map corresponding to the lower-phase values in the nucleus and tail.

Fig. 3.

Workflow for training a deep-convolutional neural network on quantitative phase images and inferring the semantic segmentation. (A) For each slide, we performed z-stacks, selecting the fields of view with intact sperm cells. We recover the phase from the four intensity images and perform halo removal to account for the partially coherent illumination. Next, we use ImageJ to manually segment the cells into the head, midpiece, and tail. We down-sample the images to match the optical resolution and perform data augmentation by rotation and flipping. Training is performed using the TensorFlow backend for Keras. To boost the performance of our network, we correct for grossly defective segmentation using the Image Segmented App (part of MATLAB). This step is substantially faster than manually annotating every cell. A final training round is then performed using all data. By evaluating the network (inference) on all images, we obtain the segmentation results for all of the cells, which are grouped into individual sperm cells using connected-coordinate analysis. Finally, these results were used to determine the relationship between the dry mass of cellular ultrastructure and ART success rates. (B) Semantic segmentation converts phase maps into a binary mask corresponding to the head, midpiece, and tail. The U-Net architecture performs well on our data, as it contains a large receptive field, well suited for the rich, broadband images typically found in microscopy. The U-Net architecture consists of a series of nonlinear operations as outlined in Materials and Methods. In our implementation, we modify the training procedure for the network by introducing dropout at the bottom of the network as well as batch-normalization on all paths. The network results in a four-channel image with the probability for each of the four classes (“head,” “midpiece,” “tail,” “background”), the largest of which assigned the label for the class.

Fig. 4.

Deep learning tracks subtle but significant differences in sperm morphology. (A) Histogram of the distribution of sperm dry-mass ratios shows that structural differences between sperm cells are relatively narrow, as evidenced by the percentage difference between the first and third quartile for the head to midpiece, head to tail, midpiece to tail are (only) 11, 24, and 17%, respectively. (B) Dry-mass maps of representative sperm cells along with semantic segmentation are labeled with their dry-mass ratios (head to midpiece [R_hm], head to tail [R_ht], midpiece to tail [R_mt]). Additionally, ↑ denotes an increase or ↓ represents a decrease in ART outcome, as determined in Fig. 5 and SI Appendix. These differences are especially difficult to visualize with conventional techniques as typical microscope images are not proportional to dry mass and the naked eye is unable to segment, integrate, and divide portions of an image (40×/0.75; SLIM).

Fig. 5.

Summary of outcomes. (A and B) Cleavage is strongly favored by a more massive tail (A), while blastocyst development is favored by a heavier head (B). (C) Summary across the five bulls for cleavage and blastocyst rates. H, head; Mid., midpiece; R_hm, head-to-midpiece ratio; R_ht, head-to-tail ratio; R_mt, midpiece-to-tail ratio; T, tail.