Methods for rapid and label-free cell assay are highly desired in life science. Single-shot diffraction imaging presents strong potentials to achieve this goal as evidenced by past experimental results using methods such as polarization diffraction imaging flow cytometry. We present here a platform of methods toward solving these problems and results of optical cell model (OCM) evaluations by calculations and analysis of cross-polarized diffraction image (p-DI) pairs. Four types of realistic OCMs have been developed with two prostate cell structures and adjustable refractive index (RI) parameters to investigate the effects of cell morphology and index distribution on calculated p-DI pairs. Image patterns have been characterized by a gray-level co-occurrence matrix (GLCM) algorithm and four GLCM parameters and linear depolarization ratio δ L have been selected to compare calculated against measured data of prostate cells. Our results show that the irregular shapes of and heterogeneity in RI distributions for organelles play significant roles in the spatial distribution of scattered light by cells in comparison to the average RI values and their differences among the organelles. Discrepancies in GLCM and δ L parameters between calculated and measured p-DI data provide useful insight for understanding light scattering by single cells and improving OCM.

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.

Development of label‐free methods for accurate classification of cells with high throughput can yield powerful tools for biological research and clinical applications. We have developed a deep neural network of DINet for extracting features from cross‐polarized diffraction image (p‐DI) pairs on multiple pixel scales to accurately classify cells in five types. A total of 6185 cells were measured by a polarization diffraction imaging flow cytometry (p‐DIFC) method followed by cell classification with DINet on p‐DI data. The averaged value and SD of classification accuracy were found to be 98.9% ± 1.00% on test data sets for 5‐fold training and test. The invariance of DINet to image translation, rotation, and blurring has been verified with an expanded p‐DI data set. To study feature‐based classification by DINet, two sets of correctly and incorrectly classified cells were selected and compared for each of two prostate cell types. It has been found that the signature features of large dissimilarities between p‐DI data of correctly and incorrectly classified cell sets increase markedly from convolutional layers 1 and 2 to layers 3 and 4. These results clearly demonstrate the importance of high‐order correlations extracted at the deep layers for accurate cell classification.