Jens-Peer Kuska

University of Leipzig, Leipzig, Saxony, Germany

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Publications (29)51.6 Total impact

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    ABSTRACT: 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.
    Brain research 01/2013; 1500:72-87. DOI:10.1016/j.brainres.2012.12.048 · 2.83 Impact Factor
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    ABSTRACT: 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. 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. 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. 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.
    The Journal of urology 03/2012; 187(5):1867-75. DOI:10.1016/j.juro.2011.12.054 · 3.75 Impact Factor
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    ABSTRACT: 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.
    PLoS ONE 07/2011; 6(7):e21960. DOI:10.1371/journal.pone.0021960 · 3.23 Impact Factor
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    ABSTRACT: Basal cell carcinoma (BCC) is the most common malignant skin cancer. For a deeper insight into the specific growth patterns of the tumorous tissue in BCC, we have focused on the development of a novel automated image-processing chain for 3D reconstruction of BCC using histopathological serial sections. For fully automatic delineation of the tumor within the tissue, we apply a fuzzy c-means segmentation method. We used a novel multi-grid form of the non-linear registration introduced by Braumann and Kuska in 2005 effectively suppressing registration runs into local minima (possibly caused by diffuse nature of the tumor). Our method was successfully applied in a proof-of-principle study for automated reconstruction.
    Experimental Dermatology 07/2010; 19(7):689-91. DOI:10.1111/j.1600-0625.2010.01100.x · 4.12 Impact Factor
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    ABSTRACT: This work presents a complete processing-chain for a 3D-reconstruction of Basal Cell Carcinoma (BCC). BCC is the most common malignant skin cancer with a high risk of local recurrence after insufficient treatment. Therefore, we have focused on the development of an automated image-processing chain for 3D-reconstruction of BCC using large histological serial sections. We introduce a novel kind of image-processing chain (core component: non-linear image registration) which is optimised for the diffuse nature of BCC. For full-automatic delineation of the tumour within the tissue we apply a fuzzy c-means segmentation method, which does not calculate a hard segmentation decision but class membership probabilities. This feature moves the binary decision tumorous vs. non-tumorous to the end of the processing chain, and it ensures smooth gradients which are needed for a consistent registration. We used a multi-grid form of the nonlinear registration effectively suppressing registration runs into local minima (possibly caused by diffuse nature of the tumour). To register the stack of images this method is applied in a new way to reduce a global drift of the image stack while registration. Our method was successfully applied in a proof-of-principle study for automated tissue volume reconstruction followed by a quantitative tumour growth analysis.
    Biomedical Image Registration, 4th International Workshop, WBIR 2010, Lübeck, Germany, July 11-13, 2010. Proceedings; 01/2010
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    ABSTRACT: The tracking of individual cells in time-lapse microscopy fa- cilitates the assessment of certain characteristics of difierent cell types. Since manual tracking of an adequate number of cells over a consider- able number of frames is tedious and sometimes not feasible, there is a vital interest in automated methods. We present a rather minimalistic approach for the tracking of unstained cells in cell culture assays. The proposed approach comprises background subtraction, an object detec- tion method based on discrete geometrical feature analysis together with a validation of the resulting graph-structures. The main advantage of this approach lies in its computational e-ciency.
    Bildverarbeitung für die Medizin 2009: Algorithmen - Systeme - Anwendungen, Proceedings des Workshops vom 22. bis 25. März 2009 in Heidelberg; 01/2009
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    ABSTRACT: Carefully studied in-situ hybridization Gene expression pat- terns (GEP) can provide a flrst glance at possible relationships among genes. Automatic comparative analysis tools are an indispensable re- quirement to manage the constantly growing amount of such GEP im- ages. We present here an automated processing pipeline for Segmenting, Classiflcation, and Clustering large-scale sets of Drosophila melanogaster GEP images that facilitates automatic GEP analysis.
    Bildverarbeitung für die Medizin 2009: Algorithmen - Systeme - Anwendungen, Proceedings des Workshops vom 22. bis 25. März 2009 in Heidelberg; 01/2009
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    ABSTRACT: 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.
    Proceedings / ICIP ... International Conference on Image Processing 12/2008; 1(1):721-724. DOI:10.1109/ICIP.2008.4711856
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    ABSTRACT: This work focuses on the segmentation of axonal structures in digital images of organotypic slice co-cultures. An image processing chain is presented, which relies on anisotropic diffusion for preprocessing of the images and the intelligent scissors method for segmentation. This method requires manual user interaction to set the starting points. To overcome this drawback, the initial parameters for the intelligent scissors are automatically extracted from the images by a graph-based approach.
    Bildverarbeitung für die Medizin 2008, Algorithmen, Systeme, Anwendungen, Proceedings des Workshops vom 6. bis 8. April 2008 in Berlin; 01/2008
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    Jens-Peer Kuska · Patrick Scheibe · Ulf-Dietrich Braumann
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    ABSTRACT: The present paper derives two new equations for the nonlin- ear registration of images based on Euler-Lagrange equations. It offers a systematic way to construct fluid extensions to registration equations based on variational principles. Nonlinear image registration has various applications in image processing ranging from three-dimensional reconstruction of serial sections over segmentation with help of image atlases to the measurement of changes during the development of a disease. The goal of image registration is to find a vector transformation u(x) so that the sample image S(x) under the transformation S(x u(x)) matches the template image T(x). Additionally, such transformation u(x) is required to satisfy some smoothness condition. One way to obtain such a transformation is to use physically motivated equations, e.g. the Navier-Lame equation for the deformation u (1) or solving the Navier-Lame equation for the velocity field ú u and to use the time integral of the velocity as the displacement field (2). The variant that uses the two fields ú u(x) and u(x) is called fluid registration. Another possibility to derive a differential equation for the displacement field is to apply a variational approach (3, 4, 5, 6) that combines various conditions for the displacement field. This approach typically yields a static, time and velocity independent equation for the displacement field u(x). To obtain a smooth con- vergence of the nonlinear equation one introduces an artificial time and solves a diffusion-like equation (7, 8). Since the displacement field is modified by a dif- fusion like process, the convergence of the variational equations is rather slow comparing to the fluid registration. The present paper offers a way to construct dynamic equations for the velocity field on the basis of variational equations without the help of fluid dynamics.
    Bildverarbeitung für die Medizin 2008, Algorithmen, Systeme, Anwendungen, Proceedings des Workshops vom 6. bis 8. April 2008 in Berlin; 01/2008
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    ABSTRACT: A new method is presented to quantify malignant changes in histological sections of prostate tissue immunohistochemically stained for prostate-specific antigen (PSA) by means of image processing. The morphological analysis of the prostate tissue uses the solidity of PSA- positive prostate tissue segments to compute a quantitative measure that turns out highly correlated with scores obtained from routine diagnosis (Gleason, Dhom).
    Bildverarbeitung für die Medizin 2008, Algorithmen, Systeme, Anwendungen, Proceedings des Workshops vom 6. bis 8. April 2008 in Berlin; 01/2008
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    Jens-Peer Kuska · Patrick Scheibe · Ulf-Dietrich Braumann
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    ABSTRACT: The present paper shows a sytematic way to derive fluid-like registration equations. The novel technique is demonstrated for the case of optical flow-based and diffusion-based regis- tration.
    Proceedings of the International Conference on Image Processing, ICIP 2008, October 12-15, 2008, San Diego, California, USA; 01/2008
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    ABSTRACT: The invasion front pattern of squamous cell carcinoma (SCC) is a conspicuous histological phenomenon, which is assessed without precise criteria. The current study was performed to introduce the classical (C(C)) and discrete compactness (C(D)) as new morphometric parameters for quantification of this pattern. A retrospective analysis of 76 surgically treated patients with cervical carcinoma was conducted and the pattern of invasion was qualitatively classified as closed, finger-like or diffuse, respectively, by two pathologists. After digitization of the histological slides with a field of view of 10.4 mm x 8.3mm, tumor areas were labeled and C(C) and C(D) were computed based on the drawings (binary images). Additionally, intraindividual variation of compactness was evaluated for 12 selected tumors. The qualitative pattern assessment by the pathologists was moderately reproducible with an interobserver agreement of 72% and a kappa coefficient of 0.44. The values of C(C) and C(D) referring to the invasion front patterns assigned by both pathologists were significantly different between the three classified groups (p< or =0.01 and p< or =0.0001), so that, both theoretically and in practice, compactness regards the same morphological feature. In due consideration of the analysis of the area under the ROC (receiver operating characteristic) curves and the variation coefficient of different tumor regions, C(D) is more suitable for practical use than C(C). Tumors with a microscopic invasion into the parametria and with lymph-vascular space invasion were found to have a lower value of C(D), which indicates a more diffuse pattern of invasion (p=0.028 and p=0.033). We conclude that the discrete compactness C(D) is a new and reproducible parameter for a computer assisted quantification of the invasion front pattern and, thus, defines a further phenotypic feature of SCC of the uterine cervix.
    Computerized Medical Imaging and Graphics 10/2007; 31(6):428-35. DOI:10.1016/j.compmedimag.2007.03.004 · 1.50 Impact Factor
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    ABSTRACT: To investigate spatial tumor invasion using ex vivo specimens and pursue a new morphometric approach for a quantitative assessment of the invasion front. Based on histologic serial sections with up to 500 slices stained with hematoxylin-eosin, volumes of interest of the tumor invasion front were 3-D reconstructed for 13 specimens from patients with squamous cell carcinoma (SCC) of the uterine cervix. Starting from very sensitive automatic tumor segmentation, 404 presumptive loci of isolated tumor islets were detected within the reconstructed volume data sets. These loci were microscopically inspected on the slides utilizing the volume date set's coordinates. A single detached tumor cell cluster within the stroma could be verified and, additionally, 4 tumor emboli within lymph vessels. The main cause of all other suspect islets (false positive segmentations) was peritumoral inflammatory response. Spatial invasion front quantification was done using discrete compactness (3-D C(D)). A comparison with 2-D C(D) values from single slides yielded strong correlation (correlation coefficient: r = 0.94; p < 0.001). Collective migration in SCC of the cervix mainly occurs per continuitatem. 2-D C(D) appears adequate and applicable for the morphometry of tumor invasion front phenotypes.
    Analytical and quantitative cytology and histology / the International Academy of Cytology [and] American Society of Cytology 10/2007; 29(5):279-90. · 0.58 Impact Factor
  • Karsten Winter · Lars H W Metz · Jens-Peer Kuska · Bernhard Frerich
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    ABSTRACT: A method for fully automated morphological and topological quantification of microvascular structures in confocal laser scanning microscopy (CLSM) volume datasets is presented. Several characteristic morphological and topological quantities are calculated in a series of image-processing steps and can be used to compare single components as well as whole networks of microvascular structures to each other. The effect of the individual image-processing steps is illustrated and characteristic quantities of measured volume datasets are presented and discussed.
    IEEE Transactions on Medical Imaging 09/2007; 26(8):1103-14. DOI:10.1109/TMI.2007.900379 · 3.80 Impact Factor
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    ABSTRACT: Malignant growth and invasiveness of cancers is a function of both intratumoral and stromal factors. The accessibility to nutrients, oxygen and growth factors, the stromal composition, and the interference with the immune system all shape the tumor invasion front. A recent study has shown a prognostic difference with respect to different invasion patterns analyzed on histological specimens of cervical cancers. The present study analyzes the spatial organization of a cervical cancer and the relation of the tumor invasion front and the infiltration with CD3(+) T-cells. From a cervical squamous cell carcinoma specimen, 84 serial sections were performed and three interleaving series were stained with hematoxylin/eosin and immunohistochemistry directed against the cervical carcinoma biomarker p16(INK4a) and the T-cell marker CD3. Sections were passed through an image processing chain to obtain a reconstructed and segmented tissue volume. For local tumor invasion front analysis the mean curvature was used, which in turn was related to the respective local minimum tumor to T-cell distance as well to a T-cell originated diffusing substance's concentration at the tumor surface. Spatial models of the tumor tissue and the infiltrating T-cells were computed. The overall discrete compactness of the tumor invasion front was 0.89, corresponding to a pathological assessment of diffuse infiltration. The comparison of the tumor invasion front with the density of T-cell infiltration revealed an increased smoothening in regions with high T-cell infiltration. We could demonstrate the spatial organization of a cervical cancer and model the interaction between infiltrating T-cells with the tumor invasion front shape. Increased smoothening in regions with high T-cell infiltration suggests that T-cells may have an influence on the shaping of the tumor invasion front, e.g., by attacking tumor cells displaying specific antigens. The applied technique allows visualization of the spatial organization of tissues and could be extended to analyze multiple stains on alternating sections.
    Cytometry Part A 05/2007; 71(5):327-33. DOI:10.1002/cyto.a.20385 · 3.07 Impact Factor
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    ABSTRACT: Multiplexed high-content cytometric analysis of cells is a prerequisite for Cytomics and Systems Biology. Slide Based Cytometry (SBC) analysis yields quantitative cell related data on various cell constituents. It allows to measure and identify in high-throughput hundred-thousands of objects and obtain cytometric data on light absorption, scatter and fluorescence signals. Selected cells of interest can be rescanned and morphologically evaluated. To be cytometric SBC measurement needs high focal depth in order to acquire the fluorescence of the whole cell. For tissue analysis section thickness of >30mum is needed to reduce cell sectioning leading in multiple labelled specimens to an overestimation of multiple stained cells due to stereology, mimicking co-expression or elevated expression that is in fact due to coincidences in the z-axis direction. By confocal sectioning and 3D-reconstruction these overlays could be eliminated but confocal D imaging is slow and the resulting data are not cytometric. To overcome this obstacle, we combined SBC analysis with confocal imaging using a Laser Scanning Cytometer (iCys, Compucyte Corp., MA). Single to triple labelled 30-120mum thick human brain sections were scanned cytometrically (up to three laser 405nm, 488nm, 633nm) and double and triple labeled cells were identified. In the second step these objects were relocated, scanned confocally and 3D-reconstructed (Mathematica®, MathGL3d). This combination of high-throughput SBC and high-resolution confocal imaging enables for unequivocal identification of multiple labelled objects and is a prerequisite for Cytomic tissue analysis, Tissomics. (Support: HBFG 036/379-1)
    Proceedings of SPIE - The International Society for Optical Engineering 03/2007; DOI:10.1117/12.698530 · 0.20 Impact Factor
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    Patrick Scheibe · Ulf-Dietrich Braumann · Jens-Peer Kuska
    Bildverarbeitung für die Medizin 2007, Algorithmen, Systeme, Anwendungen, Proceedings des Workshops vom 25.-27. März 2007 in München; 01/2007
  • The Lancet Oncology 09/2006; 7(8):698. DOI:10.1016/S1470-2045(06)70799-9 · 24.73 Impact Factor
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    ABSTRACT: 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.
    Bildverarbeitung für die Medizin 2006, Algorithmen, Systeme, Anwendungen, Proceedings des Workshops vom 19. - 21. März 2006 in Hamburg; 01/2006