High spatially sensitive quantitative phase imaging assisted with deep neural network for classification of human spermatozoa under stressed condition

Abstract

Sperm cell motility and morphology observed under the bright field microscopy are the only criteria for selecting a particular sperm cell during intracytoplasmic Sperm injection (icSi) procedure of Assisted Reproductive Technology (ART). Several factors such as oxidative stress, cryopreservation, heat, smoking and alcohol consumption, are negatively associated with the quality of sperm cell and fertilization potential due to the changing of subcellular structures and functions which are overlooked. However, bright field imaging contrast is insufficient to distinguish tiniest morphological cell features that might influence the fertilizing ability of sperm cell. We developed a partially spatially coherent digital holographic microscope (PSC-DHM) for quantitative phase imaging (QPI) in order to distinguish normal sperm cells from sperm cells under different stress conditions such as cryopreservation, exposure to hydrogen peroxide and ethanol. Phase maps of total 10,163 sperm cells (2,400 control cells, 2,750 spermatozoa after cryopreservation, 2,515 and 2,498 cells under hydrogen peroxide and ethanol respectively) are reconstructed using the data acquired from the PSC-DHM system. Total of seven feedforward deep neural networks (DNN) are employed for the classification of the phase maps for normal and stress affected sperm cells. When validated against the test dataset, the DNN provided an average sensitivity, specificity and accuracy of 85.5%, 94.7% and 85.6%, respectively. The current QPI + DNN framework is applicable for further improving ICSI procedure and the diagnostic efficiency for the classification of semen quality in regard to their fertilization potential and other biomedical applications in general. Semen quality and male fertility potential have been continuously declining all over the world 1-4. At the same time, biomedical and technical advances have made it possible to treat male infertility using assisted reproductive technology (ART) including intracytoplasmic sperm injection (ICSI). Evaluation of semen quality and ICSI procedure are the important steps for the successful outcome of ART. Generally, semen parameters evaluation

Characterization of Liposomes Using Quantitative Phase Microscopy (QPM)

“…Figure A2 in Appendix A shows the variation of the diameter with the liposome refractive index, with downward bias as the refractive index increases. Finally, it has been shown that sub-diffraction structures can be associated with size underestimation due to the possible loss of high-frequency information during image detection [52].…”

High‐resolution single‐shot phase‐shifting interference microscopy using deep neural network for quantitative phase imaging of biological samples

“…Quantitative phase imaging (QPI) is a rapidly emerging label-free technique to reconstruct quantitative information related to the refractive index and local thickness of the specimens [1]. The experimental and computational advancement in QPI is being widely adopted for extracting quantitative information of various industrial and biological specimens such as an optical waveguide, stem cells, human red blood cells (RBC), tissue sections, sperm samples, and among others [2][3][4][5]. In the past few decades, various newly developed QPI techniques have been implemented to improve the spatial resolution, spacebandwidth, temporal phase sensitivity, acquisition rate, and spatial phase sensitivity of the system [1,[6][7][8].…”

Deep Learning Based Evaluation of Spermatozoid Motility for Artificial Insemination

“…Thambawita et al [ 22 ] suggested a two-stage architecture, where an autoencoder was used to extract sperm image features, and the pre-trained Resnet-34 CNN was employed for predicting motility and morphology of sperm heads. Butola et al [ 23 ] used feedforward deep neural networks (DNNs) to recognize normal and stress-affected sperm cells. They achieved an accuracy of 85.6% on a dataset of 10,163 interferometric images of sperm cells.…”