Realistic optical cell modeling and diffraction imaging simulation for study of optical and morphological parameters of nucleus

Abstract

Coherent light scattering presents complex spatial patterns that depend on morphological and molecular features of biological cells. We present a numerical approach to establish realistic optical cell models for generating virtual cells and accurate simulation of diffraction images that are comparable to measured data of prostate cells. With a contourlet transform algorithm, it has been shown that the simulated images and extracted parameters can be used to distinguish virtual cells of different nuclear volumes and refractive indices against the orientation variation. These results demonstrate significance of the new approach for development of rapid cell assay methods through diffraction imaging. References and links 1.D. Zink, A. H. Fischer, and J. A. Nickerson, "Nuclear structure in cancer cells," Nat. Rev. Cancer 4, 677-687 (2004 IntroductionNucleus is one of the largest organelles inside eukaryotic cells, provides the site for DNA and RNA synthesis, plays critical roles in cell development. Hence it serves as one of major targets for cell assay by morphology and is especially important for detection of abnormal conditions and cancer diagnosis [1]. Optical detection through coherent light scattering offers a much valued platform for its label-free nature and capacities to extract both morphology and molecular information. Characterization of nucleus by scattered light signals thus attracts active research efforts [2][3][4][5][6][7]. Determination of cellular and nuclear morphology is fundamentally a challenging inverse problem for their complex 3D structures. For example, structural reconstruction requires large amount of measured data per cell and often expensive computation that is too long for rapid assay [8,9]. If one aims at only to distinguish cell types such as cancer from normal or apoptotic from viable cells, however, the goal may be achieved empirically with moderate amount of measured data per cell and powerful algorithms of pattern recognition. In either case, it is very useful to develop realistic optical cell models (OCMs) and accurate simulation tools for forward calculations of measured signals of scattered light. They can be employed, for example, to generate training data for algorithm development in search of the correlations between morphological features of cells and diffraction patterns of coherent light scatter.In this report, we present a numerical approach based on previous studies for establishing realistic OCMs for generating virtual cells and accurate simulation of polarized diffraction image (p-DI) data [9][10][11][12][13]. The new approach takes the advantage of 3D cell morphology and molecular information acquired from the fluorescent confocal images to produce simulated p-DI data that are comparable to the measured ones acquired with a polarization diffraction imaging flow cytometry (p-DIFC) system [14][15][16][17][18][19][20]. To demonstrate the utility of the realistic OCMs, we have investigated the effects of nuclear morphology and refractive index (RI) on di...

Resolving power of diffraction imaging with an objective: a numerical study

“…For simplicity, we limit analysis here to calculation of unpolarized DIs by S 11 (θ s , φ s ). Extension to polarized DIs is straightforward using other elements [15,18]. In the second step, the scattered light intensity proportional to S 11 (θ s , φ s ) is projected to an input plane Γ in defined in Fig.…”

“…1(A). Previously we have developed and validated a method for accurate simulation of diffraction imaging process combining a vector wave model on light scattering and a geometric model for tracing the "rays" through the imaging unit [14,15]. The new method can reproduce the diffraction images (DIs) measured at non-conjugate positions by varying angular cone of light detection for enhanced image contrast.…”

Deep learning of diffraction image patterns for accurate classification of five cell types

“…The input images were first processed by an algorithm for extraction of texture parameters followed by a classifier operating in the parameter space. Different algorithms have been explored for extracting image texture parameters, which include gray level co‐occurrence matrix (GLCM), short‐time Fourier transform, contourlet and Gabor transforms . Despite the variations in effectiveness for classification of cells in two types, the parameter based approach requires labor intensive assessment and validation.…”

Comparison of contourlet transform and gray level co-occurrence matrix for analyzing cell-scattered patterns

“…Details of segmentation and interpolation have been described elsewhere. 12,14 The reconstructed 3-D cell structure is shown in Fig. 1(b) in which the nucleus, mitochondria, and cytoplasm can be clearly observed.…”

“…This paper reports a simulation method based on an accurate process of scattered light simulation 6,[9][10][11][12] and a reconstructed real cell morphology obtained in previous studies 6,[12][13][14] to simulate the polarized cell-scattered images. The main characteristic of scattered images is pattern distribution, which can be treated as a type of frequency information.…”