Abstract
In intracytoplasmic sperm injection (ICSI), a single sperm cell is chosen and injected into an oocyte in a dish. The selection process is guided by subjective evaluation of experienced clinicians using non-quantitative optical microscopy techniques, where the cells are mostly transparent and thus their internal morphology is not well visualized. Since staining cannot be applied to enhance imaging contrast during ICSI, label-free interferometric phase microscopy (IPM) offers an alternative solution for providing the missing information. The quantitative phase maps acquired by IPM are proportional to the product between the refractive index (RI) and the average thickness of the sample. However, so far, refractometry of human sperm cells has not been fully achieved. In this paper, to perform refractometry of individual human sperm cells, we first solve the coupling problem between the RI and the thickness profile of the cell by quantitative phase imaging in two media with different RIs. We then combined IPM with fluorescence microscopy to achieve molecular specificity as a learning step. We utilized fluorescence microscopy to localize the nucleus within the RI profile of the cell, enabling refractometry of the different structures within the sperm cell head. This approach allowed us to obtain the integral RI values of the nucleus and the acrosome, demonstrating that these two organelles can be distinguished based on their RI differences. A machine learning model was trained using spatial, morphological, and textural features extracted from the label-free quantitative phase images. The model achieved high classification performance, with a sensitivity of 89% and specificity of 94%, demonstrating its potential for automated identification of subcellular structures within sperm cells. The presented study is expected to help in better morphological characterization of sperm cells, improving their selection for fertilization.