Detecting and visualizing cell phenotype differences from microscopy images using transport-based morphometry

Abstract

Modern microscopic imaging devices are able to extract more information regarding the subcellular organization of different molecules and proteins than can be obtained by visual inspection. Predetermined numerical features (descriptors) often used to quantify cells extracted from these images have long been shown useful for discriminating cell populations (e.g., normal vs. diseased). Direct visual or biological interpretation of results obtained, however, is often not a trivial task. We describe an approach for detecting and visualizing phenotypic differences between classes of cells based on the theory of optimal mass transport. The method is completely automated, does not require the use of predefined numerical features, and at the same time allows for easily interpretable visualizations of the most significant differences. Using this method, we demonstrate that the distribution pattern of peripheral chromatin in the nuclei of cells extracted from liver and thyroid specimens is associated with malignancy. We also show the method can correctly recover biologically interpretable and statistically significant differences in translocation imaging assays in a completely automated fashion.optimal transport | cell morphometry | high content screening Q uantitative analysis of cell images is extensively used in several health sciences applications (1). Scientists wishing to quantify the effects of certain drugs, genes, and other perturbations (e.g., benign vs. malignant cancer cells) routinely make use of numerical software that are capable of evaluating statistical differences between two populations of cells captured under the microscope (2). Beyond simple automation facilitating the analysis of thousands of cells, the purpose of such software is to attempt to extract information that the human visual system is unable to cope with. A well-known drawback of existing methods is that the visual interpretation of any differences found is usually hidden from the user. The popular numerical features used to quantify and compare cells, such as form factor, Gabor and Haralick texture features, color histograms, etc. (3-5), usually do not have a direct biological interpretation. The situation is even more complicated when multiple features are needed simultaneously to characterize differences between cells, given that the physical interpretation of a combination of features with different units is a nontrivial task. Consequently, statistical tests are limited to determining whether or not two or more cell populations are different. Visual interpretation of any obtained result is usually nonintuitive and difficult.Here we describe a method, which we call transport-based morphometry (TBM), that takes as input a database of presegmented cell images and outputs a representation for the same data, which can be used for simultaneous visualization and quantitative evaluation in commonplace biological domains. An a priori set of numerical features is not needed as all calculations for comparing cells are done using the entire inform...

Discovery and visualization of structural biomarkers from MRI using transport-based morphometry

“…We hypothesize that transforming MRI data using the new TBM approach can facilitate both discovery as well as visualization of discriminating differences in a manner similar to 1D and 2D signal analysis previously reported [13, 14, 15, 16, 17, 18, 19]. Ultimately, the goals of discovering objective clinical markers and understanding structure-function relationships would be facilitated by a technique that could assess structural changes underlying clinical phenotype in a fully automated manner without information loss and visualize the shifts in tissue distribution as a series of radiology images as part of a unified framework.…”

“…As previous work has demonstrated that transforming signals to the transport domain using OMT may increase their separability [13, 14, 15, 16, 17, 18, 19], we describe a framework for regression, discrimination, and blind signal separation in the transport space.…”

“…In this paper, we describe transport-based morphometry (TBM) [13, 14, 15, 16, 17], which has the potential to enable fully automated MRI analysis without loss of information. Rather than analyzing images in the image domain where they may not be easily separable, we first transform them to a domain that enhances separability.…”

“…Rather than analyzing images in the image domain where they may not be easily separable, we first transform them to a domain that enhances separability. Prior work demonstrates that when we transform 1D and 2D signals in other applications using TBM [13, 14, 15, 16, 17, 18, 19], complex and nonlinear morphology in the image domain can be described by linear classification and regression models in the transform domain. Furthermore, the key advance of TBM is that it is generative and enables direct visualization of the interface between signal classes through inverse TBM transformation [19].…”

“…As Figure 2 illustrates, unlike deformation-based approaches, OMT can match both shape and texture variations simultaneously; thus, information is not missed. However, TBM has never been applied for MRI-based detection and pattern analysis as current formulations and solutions to TBM are designed for smaller signals [13, 14, 15, 16, 17, 18]. …”