Machine learning of diffraction image patterns for accurate classification of cells modeled with different nuclear sizes.,Journal of Biophotonics

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Machine learning of diffraction image patterns for accurate classification of cells modeled with different nuclear sizes.
Journal of Biophotonics ( IF 2.0 ) Pub Date : 2020-07-03 , DOI: 10.1002/jbio.202000036
Jing Liu _{1,

2} , Yaohui Xu _{1,

2} , Wenjin Wang _{1,

3} , Yuhua Wen _{1,

3} , Heng Hong ₄ , Jun Q Lu _{1,

5} , Peng Tian _{1,

3} , Xin-Hua Hu _{1,

5}

Affiliation

Measurement of nuclear‐to‐cytoplasm (N:C) ratios plays an important role in detection of atypical and tumor cells. Yet, current clinical methods rely heavily on immunofluroescent staining and manual reading. To achieve the goal of rapid and label‐free cell classification, realistic optical cell models (OCMs) have been developed for simulation of diffraction imaging by single cells. A total of 1892 OCMs were obtained with varied nuclear volumes and orientations to calculate cross‐polarized diffraction image (p‐DI) pairs divided into three nuclear size groups of OCM_S, OCM_O and OCM_L based on three prostate cell structures. Binary classifications were conducted among the three groups with image parameters extracted by the algorithm of gray‐level co‐occurrence matrix. The averaged accuracy of support vector machine (SVM) classifier on test dataset of p‐DI was found to be 98.8% and 97.5% respectively for binary classifications of OCM_S vs OCM_O and OCM_O vs OCM_L for the prostate cancer cell structure. The values remain about the same at 98.9% and 97.8% for the smaller prostate normal cell structures. The robust performance of SVM over clustering classifiers suggests that the high‐order correlations of diffraction patterns are potentially useful for label‐free detection of single cells with large N:C ratios.

中文翻译：

衍射图像图案的机器学习，可对具有不同核尺寸的细胞进行准确分类。

核对细胞质（N：C）比的测量在检测非典型和肿瘤细胞中起着重要作用。然而，当前的临床方法严重依赖于免疫荧光染色和手动阅读。为了实现快速，无标签细胞分类的目标，已经开发了实际的光学细胞模型（OCM），用于模拟单个细胞的衍射成像。总共获得了1892个具有不同核体积和方向的OCM ，以计算交叉极化衍射图像（p-DI）对，将其分为OCM _S，OCM _O和OCM _L的三个核尺寸组基于三种前列腺细胞结构。通过灰度共生矩阵算法提取图像参数，对三组图像进行二元分类。在前列腺癌细胞结构的OCM _S与OCM _O和OCM _O与OCM _L的二元分类中，p-DI测试数据集上的支持向量机（SVM）分类器的平均准确度分别为98.8％和97.5％。对于较小的前列腺正常细胞结构，该值大致保持在98.9％和97.8％。SVM在聚类分类器上的强大性能表明，衍射图的高阶相关性可能对无标记检测具有较大N：C比的单个细胞有用。

更新日期：2020-07-03

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