Machine learning of diffraction image patterns for accurate classification of cells modeled with different nuclear sizes. Issue 9 (3rd July 2020) - Liu, Jing; Xu, Yaohui; Wang, Wenjin; Wen, Yuhua; Hong, Heng; Lu, Jun Q.; Tian, Peng; Hu, Xin‐Hua
Abstract: 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 OCMS, OCMO and OCML 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 OCMS vs OCMO and OCMO vs OCML 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. Abstract : Nuclear size provides an important marker in detecting tumor cells. We obtained 1892 realistic optical cell models from confocal image stacks of human prostate normal and cancer cells to calculate cross‐polarized diffraction image (p‐DI) pairs in three different nuclear size groups. The support vector machine (SVM) algorithm exhibits robust performance with accuracy above 97% between cells in different nuclear size groups. These results demonstrate significant potential of p‐DI data for label‐free cell classification by nuclear size.