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To assess the influence of the alkali-silica reaction (ASR) on pavement concrete 3D-CT imaging has been applied to concrete samples. Prior to imaging these samples have been drilled out of a concrete beam pre-damaged by fatigue loading. The resulting high resolution 3D-CT images consist of several gigabytes of voxels. Current desktop computers can visualize such big datasets without problems but a visual inspection or manual segmentation of features such as cracks by experts can only be carried out on a few slices. A quantitative analysis of cracks requires a segmentation of the whole specimen which could only be done by an automatic feature detection. This arises the question of the reliability of an automatic crack detection algorithm, its certainty and limitations. Does the algorithm find all cracks? Does it find too many cracks? Can parameters of that algorithm, once identified as good, be applied to other samples as well? Can ensemble computing with many crack parameters overcome the difficulties with parameter finding? By means of a crack detection algorithm based on shape recognition (template matching) these questions will be discussed. Since the author has no access to reliable ground truth data of cracks the assessment of the certainty of the automatic crack is restricted to visual inspection by experts. Therefore, an artificial dataset based on a combination of manually segmented cracks processed together with simple image processing algorithms is used to quantify the accuracy of the crack detection algorithm. Part of the evaluation of cracks in concrete samples is the knowledge of the surrounding material. The surrounding material can be used to assess the detected cracks, e.g. micro-cracks within the aggregate-matrix interface may be starting points for cracks on a macro scale. Furthermore, the knowledge of the surrounding material can help to find better parameter sets for the crack detection itself because crack characteristics may vary depending on their surrounding material. Therefore, in addition to a crack detection a complete segmentation of the sample into the components of concrete, such as aggregates, cement matrix and pores is needed. Since such a segmentation task cannot be done manually due to the amount of data, an approach utilizing convolutional neuronal networks stemming from a medical application has been applied. The learning phase requires a ground truth i.e. a segmentation of the components. This has to be created manually in a time-consuming task. However, this segmentation can be used for a quantitative evaluation of the automatic segmentation afterwards. Even though that work has been performed as a short term subtask of a bigger project funded by the German Research Foundation (DFG) this paper discusses problems which may arise in similar projects, too.
[1.2MB | id=23664 ]
iCT 2019
Session: Short talks
Thu 13:50 Auditorium 2019-03
Möglichkeiten und Grenzen automatischer Merkmalserkennung am Beispiel von Risserkennungen in 3D-CT-Aufnahmen von Betonproben
O. Paetsch11
Visualisation and Data Analysis; Konrad-Zuse-Institut Berlin (ZIB)18, Berlin, Germany
Abstract
[1MB | id=23104 ] DE
DGZfP 2018
Session: Bauwesen 2018-09
Quantitative Rissanalyse im Fahrbahndeckenbeton mit der 3D-Computertomographie
D. Meinel125, K. Ehrig128, F. Weise16, O. Paetsch211
1Division 8.5; BAM Federal Institute for Materials Research and Testing1277, Berlin, Germany
2Visualisation and Data Analysis; Konrad-Zuse-Institut Berlin (ZIB)18, Berlin, Germany
concrete, ROI tomography, in-situ-CT, 3D-CT, Beton, AKR, Feuchtetransport, automatic crack detection
Abstract
[0.7MB | id=18980 ] DE
DGZfP 2015
Session: CT Algorithmen 2016-04
3D Corrosion Detection in Time-dependent CT Images of Concrete
O. Paetsch111, D. Baum15, S. Prohaska17, K. Ehrig228, D. Meinel225, G. Ebell24
1Visualisation and Data Analysis; Konrad-Zuse-Institut Berlin (ZIB)18, Berlin, Germany
2Division 8.5; BAM Federal Institute for Materials Research and Testing1277, Berlin, Germany
CT, multi-angle radiography, defect detection, Feature Extraction, image processing, concrete, corrosion
Abstract
[0.5MB | id=18043 ]
DIR 2015
Session: Quantitative imaging and image processing 2015-08
Korrosionsverfolgung in 3D-computertomographischen Aufnahmen von Stahlbetonproben
O. Paetsch111, D. Baum15, G. Ebell24, K. Ehrig228, A. Heyn2, D. Meinel225, S. Prohaska17
1Konrad-Zuse-Institut Berlin (ZIB)18, Berlin, Germany
2Division VIII.3; BAM Federal Institute for Materials Research and Testing1277, Berlin, Germany
Computertomographie [0.4MB | id=17375 ] DE
DGZfP 2014
Session: Bauwesen 2015-03
Examination of Damage Processes in Concrete with CT
D. Meinel125, K. Ehrig128, V. L’Hostis2, B. Muzeau2, O. Paetsch311
1BAM Federal Institute for Materials Research and Testing1277, Berlin, Germany
2Laboratoire d’Etude du Comportement des Bétons et des Argiles; Commissariat Energie Atomique (CEA)287, Gif-Sur-Yvette, France
3Konrad-Zuse-Institut Berlin (ZIB)18, Berlin, Germany
X-ray computed tomography, concrete, corrosion, crack detection, 3D visualization
Abstract
[4.9MB | id=15692 ]
iCT 2014
Session: Non-destructive Testing and 3D Materials Characterisation of... 2014-06
3-D-Visualisierung und statistische Analyse von Rissen in mit Computer-Tomographie untersuchten Betonproben
O. Paetsch111, D. Baum15, D. Breßler1, K. Ehrig228, D. Meinel225, S. Prohaska1,17
1Konrad-Zuse-Institut Berlin (ZIB)18, Berlin, Germany
2Division VIII.3; BAM Federal Institute for Materials Research and Testing1277, Berlin, Germany
Radiographic Testing (RT), statistical analysis, 3D Computed Tomography, visualization, concrete structural damage, automated crack detection [1MB | id=15343 ] DE
DGZfP 2013
Session: Computertomographie 2014-03
Vergleich automatischer 3D-Risserkennungsmethoden für die quantitative Analyse der Schadensentwicklung in Betonproben mit Computertomographie
O. Paetsch111, K. Ehrig228, D. Meinel225, D. Baum15, S. Prohaska1,1,17
1Konrad-Zuse-Institut Berlin (ZIB)18, Berlin, Germany
2Division VIII.3; BAM Federal Institute for Materials Research and Testing1277, Berlin, Germany
Radiographic Testing (RT), visualization, crack detection, Visualisierung, computer tomography, template matching, Hessian eigenvalues, ZIBAmira, automated crack detection, percolation [0.9MB | id=14269 ] DE
DGZfP 2012
Session: Computertomographie 2013-05
Automated 3D Crack Detection for Analyzing Damage Processes in Concrete with Computed Tomography
O. Paetsch111, D. Baum15, K. Ehrig228, D. Meinel225, S. Prohaska1,1,17
1Konrad-Zuse-Institut Berlin (ZIB)18, Berlin, Germany
2Division VIII.3; BAM Federal Institute for Materials Research and Testing1277, Berlin, Germany
computed tomography, template matching, Hessian eigenvalues, crack statistics, visualization, crack surface, ZIBAmira [0.6MB | id=13736 ]
iCT 2012
Session: Poster - Analysis and Algorithms 2012-12
3-D-Visualisierung von Radar- und Ultraschallecho-Daten mit ZIBAmira
D. Streicher112, O. Paetsch211, R. Seiler2, S. Prohaska27, M. Krause360 [Profile of Krause] , C. Boller178
1Saarland University74, Saarbrücken, Germany
2Konrad-Zuse-Institut Berlin (ZIB)18, Berlin, Germany
3BAM Federal Institute for Materials Research and Testing1277, Berlin, Germany [0.4MB | id=12284 ] DE
DGZfP 2011
Session: Bauwesen 2012-05
Comparison of Crack Detection Methods for Analyzing Damage Processes in Concrete with Computed Tomography
K. Ehrig128, J. Goebbels153, D. Meinel125, O. Paetsch211, S. Prohaska27, V. Zobel2
1Division VIII.3; BAM Federal Institute for Materials Research and Testing1277, Berlin, Germany
2Konrad-Zuse-Institut Berlin (ZIB)18, Berlin, Germany [0.7MB | id=11150 ]
DIR 2011
Session: Poster 2011-11
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2019 Oct 7-9
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2019 Nov 13-15
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2019 Dec 4-5
10th Conference on Industrial Computed Tomography (iCT) 2020
2020 Feb 4-7
34th European Conference on Acoustic Emission Testing (EWGAE 2020)
2020 Sep 9-11
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In this work, we address the challenge of developing statistical shape models that account for the non-Euclidean nature inherent to (anatomical) shape variation and at the same time offer fast, numerically robust processing and as much invariance as possible regarding translation and rotation, i.e. Euclidean motion.
With the aim of doing that we formulate a continuous and physically motivated notion of shape space based on deformation gradients. We follow two different tracks endowing this differential representation with a Riemannian structure to establish a statistical shape model. (1) We derive a model based on differential coordinates as elements in GL(3)+. To this end, we adapt the notion of bi-invariant means employing an affine connection structure on GL(3)+. Furthermore, we perform second-order statistics based on a family of Riemannian metrics providing the most possible invariance, viz. GL(3)+-left-invariance and O(3)-right-invariance. (2) We endow the differential coordinates with a non-Euclidean structure, that stems from a product Lie group of stretches and rotations. This structure admits a bi-invariant metric and thus allows for a consistent analysis via manifold-valued Riemannian statistics. This work further presents a novel shape representation based on discrete fundamental forms that is naturally invariant under Euclidean motion, namely the fundamental coordinates. We endow this representation with a Lie group structure that admits bi-invariant metrics and therefore allows for consistent analysis using manifold-valued statistics based on the Riemannian framework. Furthermore, we derive a simple, efficient, robust, yet accurate (i.e. without resorting to model approximations) solver for the inverse problem that allows for interactive applications. Beyond statistical shape modeling the proposed framework is amenable for surface processing such as quasi-isometric flattening. Additionally, the last part of the thesis aims on shape-based, continuous disease stratification to provide means that objectify disease assessment over the current clinical practice of ordinal grading systems. Therefore, we derive the geodesic B-score, a generalization of the of the Euclidean B-score, in order to assess knee osteoarthritis. In this context we present a Newton-type fixed point iteration for projection onto geodesics in shape space. On the application side, we show that the derived geodesic B-score features, in comparison to its Euclidean counterpart, an improved predictive performance on assessing the risk of total knee replacement surgery.
The analysis of brain networks is central to neurobiological research. In this context the following tasks often arise: (1) understand the cellular composition of a reconstructed neural tissue volume to determine the nodes of the brain network; (2) quantify connectivity features statistically; and (3) compare these to predictions of mathematical models. We present a framework for interactive, visually supported accomplishment of these tasks. Its central component, the stratification matrix viewer, allows users to visualize the distribution of cellular and/or connectional properties of neurons at different levels of aggregation. We demonstrate its use in four case studies analyzing neural network data from the rat barrel cortex and human temporal cortex.
3D shapes provide substantially more information than 2D images. However, the acquisition of 3D shapes is sometimes very difficult or even impossible in comparison with acquiring 2D images, making it necessary to derive the 3D shape from 2D images. Although this is, in general, a mathematically ill-posed problem, it might be solved by constraining the problem formulation using prior information. Here, we present a new approach based on Kendall’s shape space to reconstruct 3D shapes from single monocular 2D images. The work is motivated by an application to study the feeding behavior of the basking shark, an endangered species whose massive size and mobility render 3D shape data nearly impossible to obtain, hampering understanding of their feeding behaviors and ecology. 2D images of these animals in feeding position, however, are readily available. We compare our approach with state-of-the-art shape-based approaches both on human stick models and on shark head skeletons. Using a small set of training shapes, we show that the Kendall shape space approach is substantially more robust than previous methods and always results in plausible shapes. This is essential for the motivating application in which specimens are rare and therefore only few training shapes are available.
Statistical shape modeling aims at capturing shape variations of an anatomical structure that occur within a given population. Shape models are employed in many tasks, such as shape reconstruction and image segmentation, but also shape generation and classification. Existing shape priors either require dense correspondence between training examples or lack robustness and topological guarantees. We present FlowSSM, a novel shape modeling approach that learns shape variability without requiring dense correspondence between training instances. It relies on a hierarchy of continuous deformation flows, which are parametrized by a neural network. Our model outperforms state-of-the-art methods in providing an expressive and robust shape prior for distal femur and liver. We show that the emerging latent representation is discriminative by separating healthy from pathological shapes. Ultimately, we demonstrate its effectiveness on two shape reconstruction tasks from partial data. Our source code is publicly available (https://github.com/davecasp/flowssm).
The neurons in the cerebral cortex are not randomly interconnected. This specificity in wiring can result from synapse formation mechanisms that connect neurons depending on their electrical activity and genetically defined identity. Here, we report that the morphological properties of the neurons provide an additional prominent source by which wiring specificity emerges in cortical networks. This morphologically determined wiring specificity reflects similarities between the neurons’ axo-dendritic projections patterns, the packing density and cellular diversity of the neuropil. The higher these three factors are the more recurrent is the topology of the network. Conversely, the lower these factors are the more feedforward is the network’s topology. These principles predict the empirically observed occurrences of clusters of synapses, cell type-specific connectivity patterns, and nonrandom network motifs. Thus, we demonstrate that wiring specificity emerges in the cerebral cortex at subcellular, cellular and network scales from the specific morphological properties of its neuronal constituents.
Reconstructing anatomical shapes from sparse or partial measurements relies on prior knowledge of shape variations that occur within a given population. Such shape priors are learned from example shapes, obtained by segmenting volumetric medical images. For existing models, the resolution of a learned shape prior is limited to the resolution of the training data. However, in clinical practice, volumetric images are often acquired with highly anisotropic voxel sizes, e.g. to reduce image acquisition time in MRI or radiation exposure in CT imaging. The missing shape information between the slices prohibits existing methods to learn a high-resolution shape prior. We introduce a method for high-resolution shape reconstruction from sparse measurements without relying on high-resolution ground truth for training. Our method is based on neural implicit shape representations and learns a continuous shape prior only from highly anisotropic segmentations. Furthermore, it is able to learn from shapes with a varying field of view and can reconstruct from various sparse input configurations. We demonstrate its effectiveness on two anatomical structures: vertebra and femur, and successfully reconstruct high-resolution shapes from sparse segmentations, using as few as three orthogonal slices.
In many applications, geodesic hierarchical models are adequate for the study of temporal observations.
We employ such a model derived for manifold-valued data to Kendall's shape space.
In particular, instead of the Sasaki metric, we adapt a functional-based metric, which increases the computational efficiency and does not require the implementation of the curvature tensor. We propose the corresponding variational time discretization of geodesics
and employ the approach for longitudinal analysis of 2D rat skulls shapes as well as 3D shapes derived from an imaging study on osteoarthritis. Particularly, we perform hypothesis test and estimate the mean trends.
Morphomatics is an open-source Python library for (statistical) shape analysis developed within the geometric data analysis and processing research group at Zuse Institute Berlin. It contains prototype implementations of intrinsic manifold-based methods that are highly consistent and avoid the influence of unwanted effects such as bias due to arbitrary choices of coordinates.