论文标题
高空间敏感的定量相成像有助于深度神经网络在压力条件下对人类精子进行分类
High spatially sensitive quantitative phase imaging assisted with deep neural network for classification of human spermatozoa under stressed condition
论文作者
论文摘要
在明亮的场显微镜下观察到的精子细胞运动和形态是在胞浆内精子注射（ICSI）过程中选择特定精子细胞（ICSI）程序（ART）的唯一标准。诸如氧化应激，冷冻保存，热量，吸烟和饮酒等因素与精子细胞的质量和受精潜力呈负相关，这是由于忽略的亚细胞结构和功能的变化而导致的。明亮的田间成像对比度不足以区分可能影响精子细胞受精能力的最细细胞特征。我们开发了一种部分相干的数字全息显微镜（PSC-DHM），用于定量相成像（QPI），以将正常的精子细胞与不同应力条件下的精子细胞区分开，例如冷冻保存，诸如冷冻氢，过氧化氢和乙醇，无需任何标记。使用从PSC-DHM系统中获取的数据，重建了10,163个精子细胞（冷冻保存后2,750个对照细胞，2,750个精子，分别为2,515和2,498个细胞）。总共采用了七个进食深度神经网络（DNN）来分类正常和应激影响精子细胞的相位图。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84.88％，95.03％和85％。当前的方法可以应用定量信息的DNN和QPI技术，以进一步改善ICSI程序和诊断效率，以分类精液质量在其受精潜力和其他生物医学应用方面的分类。
Sperm cell motility and morphology observed under the bright field microscopy are the only criteria for selecting 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 sub-cellular structures and functions which are overlooked. A 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 without any labeling. Phase maps of 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 PSC-DHM system. Total of seven feedforward deep neural networks (DNN) were 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 84.88%, 95.03% and 85%, respectively. The current approach DNN and QPI techniques of quantitative information can be applied for further improving ICSI procedure and the diagnostic efficiency for the classification of semen quality in regards to their fertilization potential and other biomedical applications in general.
