High spatially sensitive quantitative phase imaging assisted with deep neural network for classification of human spermatozoa under stressed condition
High spatially sensitive quantitative phase imaging assisted with deep neural network for classification of human spermatozoa under stressed condition
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DOI：
10.1038/s41598-020-69857-4
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发表时间：
2020-08-04
影响因子：
4.6
通讯作者：
Ahluwalia, Balpreet Singh
中科院分区：
文献类型：
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作者：
Butola, Ankit;Popova, Daria;Ahluwalia, Balpreet Singh
在辅助生殖技术（ART）的卵胞浆内单精子注射（ICSI）过程中，在明视野显微镜下观察到的精子细胞活力和形态是选择特定精子的唯一标准。氧化应激、冷冻保存、高温、吸烟和饮酒等因素对精子质量和受精能力的影响，往往因其亚细胞结构和功能的改变而被忽视。然而，明场成像对比度不足以区分最微小的形态细胞特征，可能会影响精子的可重复性。我们开发了一种用于定量相位成像（QPI）的部分空间相干数字全息显微镜（PSC-DHM），以区分正常精子细胞和精子细胞在不同的应激条件下，如冷冻保存，暴露于过氧化氢和乙醇。使用从PSC-DHM系统获得的数据重建总共10，163个精子细胞（2，400个对照细胞，2，750个冷冻保存后的精子，分别在过氧化氢和乙醇下的2，515和2，498个细胞）的相位图。总共七个前馈深度神经网络（DNN）被用于正常和应激影响的精子细胞的相位图的分类。当针对测试数据集进行验证时，DNN的平均灵敏度、特异性和准确性分别为85.5%、94.7%和85.6%。目前的QPI+DNN框架适用于进一步改善ICSI程序和精液质量分类的诊断效率，包括受精潜力和其他生物医学应用。
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.