Jens-Peer Kuska

Three-dimensional reconstruction and quantification of cervical carcinoma invasion fronts from histological serial sections

The analysis of the three-dimensional (3-D) structure of tumoral invasion fronts of carcinoma of the uterine cervix is the prerequisite for understanding their architectural-functional relationship. The variation range of the invasion patterns known so far reaches from a smooth tumor-host boundary surface to more diffusely spreading patterns, which all are supposed to have a different prognostic relevance. As a very decisive limitation of previous studies, all morphological assessments just could be done verbally referring to single histological sections. Therefore, the intention of this paper is to get an objective quantification of tumor invasion based on 3-D reconstructed tumoral tissue data. The image processing chain introduced here is capable to reconstruct selected parts of tumor invasion fronts from histological serial sections of remarkable extent (90-500 slices). While potentially gaining good accuracy and reasonably high resolution, microtome cutting of large serial sections especially may induce severe artifacts like distortions, folds, fissures or gaps. Starting from stacks of digitized transmitted light color images, an overall of three registration steps are the main parts of the presented algorithm. By this, we achieved the most detailed 3-D reconstruction of the invasion of solid tumors so far. Once reconstructed, the invasion front of the segmented tumor is quantified using discrete compactness.

Influence of the boundary conditions on the result of non-linear image registration

The focus of many non-parametric image registration algorithms lies on the solution of non-linear partial differential equations. We offer a simple solution procedure therefor based on discrete Fourier transform. Boundary conditions can strongly influence the result of the registration. The issue is investigated on the example of non-linear curvature-based registration.

Quantitative analysis of astrogliosis in drug-dependent humans.

Drug addiction is a chronic, relapsing disease caused by neurochemical and molecular changes in the brain. In this human autopsy study qualitative and quantitative changes of glial fibrillary acidic protein (GFAP)-positive astrocytes in the hippocampus of 26 lethally intoxicated drug addicts and 35 matched controls are described. The morphological characterization of these cells reflected alterations representative for astrogliosis. But, neither quantification of GFAP-positive cells nor the Western blot analysis indicated statistical significant differences between drug fatalities versus controls. However, by semi-quantitative scoring a significant shift towards higher numbers of activated astrocytes in the drug group was detected. To assess morphological changes quantitatively, graph-based representations of astrocyte morphology were obtained from single cell images captured by confocal laser scanning microscopy. Their underlying structures were used to quantify changes in astroglial fibers in an automated fashion. This morphometric analysis yielded significant differences between the investigated groups for four different measures of fiber characteristics (Euclidean distance, graph distance, number of graph elements, fiber skeleton distance), indicating that, e.g., astrocytes in drug addicts on average exhibit significant elongation of fiber structures as well as two-fold increase in GFAP-positive fibers as compared with those in controls. In conclusion, the present data show characteristic differences in morphology of hippocampal astrocytes in drug addicts versus controls and further supports the involvement of astrocytes in human pathophysiology of drug addiction. The automated quantification of astrocyte morphologies provides a novel, testable way to assess the fiber structures in a quantitative manner as opposed to standard, qualitative descriptions.

Spatial Organization of Mesenchymal Stem Cells In Vitro—Results from a New Individual Cell-Based Model with Podia

Therapeutic application of mesenchymal stem cells (MSC) requires their extensive in vitro expansion. MSC in culture typically grow to confluence within a few weeks. They show spindle-shaped fibroblastoid morphology and align to each other in characteristic spatial patterns at high cell density. We present an individual cell-based model (IBM) that is able to quantitatively describe the spatio-temporal organization of MSC in culture. Our model substantially improves on previous models by explicitly representing cell podia and their dynamics. It employs podia-generated forces for cell movement and adjusts cell behavior in response to cell density. At the same time, it is simple enough to simulate thousands of cells with reasonable computational effort. Experimental sheep MSC cultures were monitored under standard conditions. Automated image analysis was used to determine the location and orientation of individual cells. Our simulations quantitatively reproduced the observed growth dynamics and cell-cell alignment assuming cell density-dependent proliferation, migration, and morphology. In addition to cell growth on plain substrates our model captured cell alignment on micro-structured surfaces. We propose a specific surface micro-structure that according to our simulations can substantially enlarge cell culture harvest. The ‘tool box’ of cell migratory behavior newly introduced in this study significantly enhances the bandwidth of IBM. Our approach is capable of accommodating individual cell behavior and collective cell dynamics of a variety of cell types and tissues in computational systems biology.

Classifying Prostate Cancer Malignancy by Quantitative Histomorphometry

PURPOSE Prostate cancer is routinely graded according to the Gleason grading scheme. This scheme is predominantly based on the textural appearance of aberrant glandular structures. Gleason grade is difficult to standardize and often leads to discussion due to interrater and intrarater disagreement. Thus, we investigated whether digital image based automated quantitative histomorphometry could be used to achieve a more standardized, reproducible classification outcome. MATERIALS AND METHODS In a proof of principle study we developed a method to evaluate digitized histological images of single prostate cancer regions in hematoxylin and eosin stained sections. Preprocessed color images were subjected to color deconvolution, followed by the binarization of obtained hematoxylin related image channels. Highlighted neoplastic epithelial gland related objects were morphometrically assessed by a classifier based on 2 calculated quantitative and objective geometric measures, that is inverse solidity and inverse compactness. The procedure was then applied to the prostate cancer probes of 125 patients. Each probe was independently classified for Gleason grade 3, 4 or 5 by an experienced pathologist blinded to image analysis outcome. RESULTS Together inverse compactness and inverse solidity were adequate discriminatory features for a powerful classifier that distinguished Gleason grade 3 from grade 4/5 histology. The classifier was robust on sensitivity analysis. CONCLUSIONS Results suggest that quantitative and interpretable measures can be obtained from image based analysis, permitting algorithmic differentiation of prostate Gleason grades. The method must be validated in a large independent series of specimens.

A New Equation for Nonlinear Image Registration with Control over the Vortex Structure in the Displacement Field

The goal of nonparametric image registration lies in the solution of highly nonlinear partial differential equations. We present a new partial differential equation for the nonlinear image registration that can be used for the registration of images with textured and complex shaped motifs. The new equation allows to control the vortex structure in the registration field. For many image registration problems, the required transformation should not contain vortices. The application field of the new equation is not restricted to biomedical imaging.

Graph-Based Quantification of Astrocytes

Astroglial cells in the central nervous system (CNS) are able to change their morphology and shape after different kinds of stimuli. We have developed a method for the structural description of astrocytes based on their representation as undirected simple graphs. The underlying image processing chain and the algorithm for the graph construction are presented and the graph parameters for the quantitative structural description of the astrocytes are discussed.

Compression of isosurfaces for structured volumes with context modelling

In many applications surfaces with a large number of primitives occur. Geometry compression reduces storage space and transmission time for such models. A special case is given by polygonal isosurfaces generated from gridded volume data. However most current state-of-the-art geometry compression systems do not capitalize on the structure that is characteristic of such isosurfaces, namely that the surfaces are defined by a set of vertices on edges of the grid. In a previous paper we proposed a compression method for isosurfaces that exploits this feature. In this paper we use the same coding approach, however, including context models for the encoding of the symbol streams. We report improved compression ratios for complex isosurfaces from a CT scan of a human head Our coder outperformed state-of-the-art general purpose geometry compression methods. We also report results obtained by two predictive coding methods based on least squares function fitting and a surface relaxation algorithm.

Process flow for classification and clustering of fruit fly gene expression patterns

The rapidly growing collection of fruit fly embryo images makes automated Image Segmentation and classification an indispensable requirement for a large-scale analysis of in situ hybridization (ISH) - gene expression patterns (GEP). We present here such an automated process flow for segmenting, classification, and clustering large-scale sets of Drosophila melanogaster GEP that is capable of dealing with most of the complications implicated in the images.

Registration of Histologic Colour Images of Different Staining

We have focused our interest on the registration of brightfield transmitted light microscopy images with respect to different histological stainings. For this kind of registration problem we have developed a new segmentation procedure. Based on the obtained consistent segmentations, a nonlinear registration transformation is computed. The applied registration procedure uses a curvature-based nonlinear partial differential equation in order to find the appropriate mapping between the images. Finally, we present an example for the registration of images of two consecutive histological sections from a uterine cervix specimen, whereas one section stained with p16INK4a was mapped onto another with H&E staining.