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  • 1
    Online Resource
    Online Resource
    Automatic Classification of Cancerous Tissue in Laserendomicroscopy Images of the Oral Cavity using Deep Learning
    Springer Science and Business Media LLC ; 2017
    In:  Scientific Reports Vol. 7, No. 1 ( 2017-09-20)
    Details
    In: Scientific Reports, Springer Science and Business Media LLC, Vol. 7, No. 1 ( 2017-09-20)
    Abstract: Oral Squamous Cell Carcinoma (OSCC) is a common type of cancer of the oral epithelium. Despite their high impact on mortality, sufficient screening methods for early diagnosis of OSCC often lack accuracy and thus OSCCs are mostly diagnosed at a late stage. Early detection and accurate outline estimation of OSCCs would lead to a better curative outcome and a reduction in recurrence rates after surgical treatment. Confocal Laser Endomicroscopy (CLE) records sub-surface micro-anatomical images for in vivo cell structure analysis. Recent CLE studies showed great prospects for a reliable, real-time ultrastructural imaging of OSCC in situ . We present and evaluate a novel automatic approach for OSCC diagnosis using deep learning technologies on CLE images. The method is compared against textural feature-based machine learning approaches that represent the current state of the art. For this work, CLE image sequences (7894 images) from patients diagnosed with OSCC were obtained from 4 specific locations in the oral cavity, including the OSCC lesion. The present approach is found to outperform the state of the art in CLE image recognition with an area under the curve (AUC) of 0.96 and a mean accuracy of 88.3% (sensitivity 86.6%, specificity 90%).
    Type of Medium: Online Resource
    ISSN: 2045-2322
    URL: Article
    DOI: 10.1038/s41598-017-12320-8
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2017
    detail.hit.zdb_id: 2615211-3
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  • 2
    Online Resource
    Online Resource
    Analysis of Forming Limits in Sheet Metal Forming with Pattern Recognition Methods. Part 1: Characterization of Onset of Necking and Expert Evaluation
    MDPI AG ; 2018
    In:  Materials Vol. 11, No. 9 ( 2018-08-21), p. 1495-
    Details
    In: Materials, MDPI AG, Vol. 11, No. 9 ( 2018-08-21), p. 1495-
    Abstract: In automotive manufacturing, high strength materials, and aluminum alloys are widely used to address the requirement of ensuring a lightweight car body and correspondingly, reducing pollution. In this context of complexity of materials and structures, an optimized process design with finite element analyses (FEA) is mandatory, as well as a correct definition of the material forming limits. For this purpose, in sheet metal forming, the forming limit curve (FLC) is used. The FLC is defined by the onset of necking. The standard evaluation method according to DIN EN ISO 12004-2 is based on the cross-section method and assumes that the failure occurs due to a clear localized necking. However, this approach has its limitations, specifically in the case of brittle materials that do not exhibit a distinct necking phase. To overcome this challenge, a pattern recognition-based evaluation is proposed. Although pattern recognition and machine learning techniques have been widely employed in the medical field, few studies have investigated them in the context of analyzing metal sheet forming limits. The application of pattern recognition in metal forming is subject to the exact definition of the forming behaviors. Thereby, it is challenging to relate patterns on the strain distribution during Nakajima tests with the onset of necking for the FLC determination. Thus, the first approach was based on the crack evaluation, since this class is well-defined. However, of substantial interest is the evaluation of the general material instabilities that precede failure. Therefore, in the present study, the analysis of the material behavior during stretching is conducted in order to characterize instability classes. The results of Nakajima tests are investigated using an optical measurement system. A conventional pattern recognition approach based on texture features, considering the outcomes of expert interviews for the definition of classes is used for the FLC determination. Moreover, an analysis of the validity of the supervised learning is conducted. The results show a good prediction of the onset of necking, even for high strength materials with a recall of up to 92%. Some deviations are observed in the determination of the diffuse necking. The discrepancies of the different experts’ prognoses highlight the user-dependency of the FLC, suggesting further investigations with an data-driven approach, could be beneficial.
    Type of Medium: Online Resource
    ISSN: 1996-1944
    URL: Article
    DOI: 10.3390/ma11091495
    Language: English
    Publisher: MDPI AG
    Publication Date: 2018
    detail.hit.zdb_id: 2487261-1
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  • 3
    Online Resource
    Online Resource
    Chemokine/Cytokine Levels Correlate with Organ Involvement in PR3-ANCA-Associated Vasculitis
    MDPI AG ; 2021
    In:  Journal of Clinical Medicine Vol. 10, No. 12 ( 2021-06-19), p. 2715-
    Details
    In: Journal of Clinical Medicine, MDPI AG, Vol. 10, No. 12 ( 2021-06-19), p. 2715-
    Abstract: Background: ANCA-associated vasculitis (AAV) is a rare small vessel disease characterized by multi-organ involvement. Biomarkers that can measure specific organ involvement are missing. Here, we ask whether certain circulating cytokines and chemokines correlate with renal involvement and if distinct cytokine/chemokine patterns can differentiate between renal, ear/nose/throat, joints, and lung involvement of AAV. Methods: Thirty-two sets of Birmingham vasculitis activity score (BVAS), PR3-ANCA titers, laboratory marker, and different cytokines were obtained from 17 different patients with AAV. BVAS, PR3-ANCA titers, laboratory marker, and cytokine concentrations were correlated to different organ involvements in active AAV. Results: Among patients with active PR3-AAV (BVAS 〉 0) and kidney involvement we found significant higher concentrations of chemokine ligand (CCL)-1, interleukin (IL)-6, IL21, IL23, IL-28A, IL33, monocyte chemoattractant protein 2 (MCP2), stem cell factor (SCF), thymic stromal lymphopoietin (TSLP), and thrombopoietin (TPO) compared to patients without PR3-ANCA-associated glomerulonephritis. Patients with ear, nose, and throat involvement expressed higher concentrations of MCP2 and of the (C-X-C motif) ligand-12 (CXCL-12) compared to patients with active AAV and no involvement of these organs. Conclusion: We identified distinct cytokine patterns for renal manifestation and for ear, nose and throat involvement of PR3-AAV. Distinct plasma cytokines might be used as non-invasive biomarkers of organ involvement in AAV.
    Type of Medium: Online Resource
    ISSN: 2077-0383
    URL: Article
    DOI: 10.3390/jcm10122715
    Language: English
    Publisher: MDPI AG
    Publication Date: 2021
    detail.hit.zdb_id: 2662592-1
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  • 4
    Online Resource
    Online Resource
    Front Cover: Synthetic Image Rendering Solves Annotation Problem in Deep Learning Nanoparticle Segmentation (Small Methods 7/2021)
    Wiley ; 2021
    In:  Small Methods Vol. 5, No. 7 ( 2021-07)
    Details
    In: Small Methods, Wiley, Vol. 5, No. 7 ( 2021-07)
    Type of Medium: Online Resource
    ISSN: 2366-9608 , 2366-9608
    URL: Issue
    URL: Article
    DOI: 10.1002/smtd.v5.7
    DOI: 10.1002/smtd.202170028
    Language: English
    Publisher: Wiley
    Publication Date: 2021
    detail.hit.zdb_id: 2884448-8
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  • 5
    Online Resource
    Online Resource
    Author Correction: Novel diagnostic and therapeutic techniques reveal changed metabolic profiles in recurrent focal segmental glomerulosclerosis
    Springer Science and Business Media LLC ; 2021
    In:  Scientific Reports Vol. 11, No. 1 ( 2021-05-17)
    Details
    In: Scientific Reports, Springer Science and Business Media LLC, Vol. 11, No. 1 ( 2021-05-17)
    Abstract: An amendment to this paper has been published and can be accessed via a link at the top of the paper.
    Type of Medium: Online Resource
    ISSN: 2045-2322
    URL: Article
    DOI: 10.1038/s41598-021-89610-9
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2021
    detail.hit.zdb_id: 2615211-3
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  • 6
    Online Resource
    Online Resource
    X‐ray microscopy and automatic detection of defects in through silicon vias in three‐dimensional integrated circuits
    Wiley ; 2022
    In:  Engineering Reports Vol. 4, No. 12 ( 2022-12)
    Details
    In: Engineering Reports, Wiley, Vol. 4, No. 12 ( 2022-12)
    Abstract: Through silicon vias (TSVs) are a key enabling technology for interconnection and realization of complex three‐dimensional integrated circuit (3D‐IC) components. In order to perform failure analysis without the need of destructive sample preparation, x‐ray microscopy (XRM) is a rising method of analyzing the internal structure of samples. However, there is still a lack of evaluated scan recipes or best practices regarding XRM parameter settings for the study of TSVs in the current state of literature. There is also an increased interest in automated machine learning and deep learning approaches for qualitative and quantitative inspection processes in recent years. Especially deep learning based object detection is a well‐known methodology for fast detection and classification capable of working with large volumetric XRM datasets. Therefore, a combined XRM and deep learning object detection workflow for automatic micrometer accurate defect location on liner‐TSVs was developed throughout this work. Two measurement setups including detailed information about the used parameters for either full IC device scan or detailed TSV scan were introduced. Both are able to depict delamination defects and finer structures in TSVs with either a low or high resolution. The combination of a 0.4 objective with a beam voltage of 40 kV proved to be a good combination for achieving optimal imaging contrast for the full‐device scan. However, detailed TSV scans have demonstrated that the use of a 20 objective along with a beam voltage of 140 kV significantly improves image quality. A database with 30,000 objects was created for automated data analysis, so that a well‐established object recognition method for automated defect analysis could be integrated into the process analysis. This RetinaNet‐based object detection method achieves a very strong average precision of 0.94. It supports the detection of erroneous TSVs in both top view and side view, so that defects can be detected at different depths. Consequently, the proposed workflow can be used for failure analysis, quality control or process optimization in R & D environments.
    Type of Medium: Online Resource
    ISSN: 2577-8196 , 2577-8196
    URL: Issue
    URL: Article
    DOI: 10.1002/eng2.v4.12
    DOI: 10.1002/eng2.12520
    Language: English
    Publisher: Wiley
    Publication Date: 2022
    detail.hit.zdb_id: 2947569-7
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  • 7
    Online Resource
    Online Resource
    Temporal and Spatial Detection of the Onset of Local Necking and Assessment of its Growth Behavior
    MDPI AG ; 2020
    In:  Materials Vol. 13, No. 11 ( 2020-05-26), p. 2427-
    Details
    In: Materials, MDPI AG, Vol. 13, No. 11 ( 2020-05-26), p. 2427-
    Abstract: This study proposes a method for the temporal and spatial determination of the onset of local necking determined by means of a Nakajima test set-up for a DC04 deep drawing and a DP800 dual-phase steel, as well as an AA6014 aluminum alloy. Furthermore, the focus lies on the observation of the progress of the necking area and its transformation throughout the remainder of the forming process. The strain behavior is learned by a machine learning approach on the basis of the images when the process is close to material failure. These learned failure characteristics are transferred to new forming sequences, so that critical areas indicating material failure can be identified at an early stage, and consequently enable the determination of the beginning of necking and the analysis of the necking area. This improves understanding of the necking behavior and facilitates the determination of the evaluation area for strain paths. The growth behavior and traceability of the necking area is objectified by the proposed weakly supervised machine learning approach, thereby rendering a heuristic-based determination unnecessary. Furthermore, a simultaneous evaluation on image and pixel scale is provided that enables a distinct selection of the failure quantile of the probabilistic forming limit curve.
    Type of Medium: Online Resource
    ISSN: 1996-1944
    URL: Article
    DOI: 10.3390/ma13112427
    Language: English
    Publisher: MDPI AG
    Publication Date: 2020
    detail.hit.zdb_id: 2487261-1
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  • 8
    Online Resource
    Online Resource
    Analysis of Forming Limits in Sheet Metal Forming with Pattern Recognition Methods. Part 2: Unsupervised Methodology and Application
    MDPI AG ; 2018
    In:  Materials Vol. 11, No. 10 ( 2018-10-03), p. 1892-
    Details
    In: Materials, MDPI AG, Vol. 11, No. 10 ( 2018-10-03), p. 1892-
    Abstract: The forming limit curve (FLC) is used in finite element analysis (FEA) for the modeling of onset of sheet metal instability during forming. The FLC is usually evaluated by achieving forming measurements with optical measurement system during Nakajima tests. Current evaluation methods such as the standard method according to DIN EN ISO 12004-2 and time-dependent methods limit the evaluation range to a fraction of the available information and show weaknesses in the context of brittle materials that do not have a pronounced constriction phase. In order to meet these challenges, a supervised pattern recognition method was proposed, whose results depend on the quality of the expert annotations. In order to alleviate this dependence on experts, this study proposes an unsupervised classification approach that does not require expert annotations and allows a probabilistic evaluation of the onset of localized necking. For this purpose, the results of the Nakajima tests are examined with an optical measuring system and evaluated using an unsupervised classification method. In order to assess the quality of the results, a comparison is made with the time-dependent method proposed by Volk and Hora, as well as expert annotations, while validated with metallographic investigations. Two evaluation methods are presented, the deterministic FLC, which provides a lower and upper limit for the onset of necking, and a probabilistic FLC, which allows definition of failure quantiles. Both methods provide a necking range that shows good correlation with the expert opinion as well as the results of the time-dependent method and metallographic examinations.
    Type of Medium: Online Resource
    ISSN: 1996-1944
    URL: Article
    DOI: 10.3390/ma11101892
    Language: English
    Publisher: MDPI AG
    Publication Date: 2018
    detail.hit.zdb_id: 2487261-1
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  • 9
    Online Resource
    Online Resource
    A New Approach to the Evaluation of Forming Limits in Sheet Metal Forming
    Trans Tech Publications, Ltd. ; 2015
    In:  Key Engineering Materials Vol. 639 ( 2015-3), p. 333-338
    Details
    In: Key Engineering Materials, Trans Tech Publications, Ltd., Vol. 639 ( 2015-3), p. 333-338
    Abstract: The forming limit diagram (FLD) is at the moment the most important method for the prediction of failure within sheet metal forming operations. Key idea is the detection of the onset of necking in dependency of different sample geometry. Whereas the standardized evaluation methods provides very robust and reliable results for conventional materials like deep drawing steels, the determined forming limits for modern light materials are often too conservative due to the different failure behavior. Therefore, within this contribution a new and innovative approach for the identification of the onset of necking will be presented. By using a pattern recognition-based approach in combination with an optical strain measurement system the complete strain history during the test can be evaluated. The principal procedure as well as the first promising results are presented and discussed.
    Type of Medium: Online Resource
    ISSN: 1662-9795
    URL: Issue
    URL: Article
    DOI: 10.4028/www.scientific.net/KEM.639
    DOI: 10.4028/www.scientific.net/KEM.639.333
    Language: Unknown
    Publisher: Trans Tech Publications, Ltd.
    Publication Date: 2015
    detail.hit.zdb_id: 2073306-9
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  • 10
    Online Resource
    Online Resource
    Novel diagnostic and therapeutic techniques reveal changed metabolic profiles in recurrent focal segmental glomerulosclerosis
    Springer Science and Business Media LLC ; 2021
    In:  Scientific Reports Vol. 11, No. 1 ( 2021-02-25)
    Details
    In: Scientific Reports, Springer Science and Business Media LLC, Vol. 11, No. 1 ( 2021-02-25)
    Abstract: Idiopathic forms of Focal Segmental Glomerulosclerosis (FSGS) are caused by circulating permeability factors, which can lead to early recurrence of FSGS and kidney failure after kidney transplantation. In the past three decades, many research endeavors were undertaken to identify these unknown factors. Even though some potential candidates have been recently discussed in the literature, “the” actual factor remains elusive. Therefore, there is an increased demand in FSGS research for the use of novel technologies that allow us to study FSGS from a yet unexplored angle. Here, we report the successful treatment of recurrent FSGS in a patient after living-related kidney transplantation by removal of circulating factors with CytoSorb apheresis. Interestingly, the classical published circulating factors were all in normal range in this patient but early disease recurrence in the transplant kidney and immediate response to CytoSorb apheresis were still suggestive for pathogenic circulating factors. To proof the functional effects of the patient’s serum on podocytes and the glomerular filtration barrier we used a podocyte cell culture model and a proteinuria model in zebrafish to detect pathogenic effects on the podocytes actin cytoskeleton inducing a functional phenotype and podocyte effacement. We then performed Raman spectroscopy in the  〈  50 kDa serum fraction, on cultured podocytes treated with the FSGS serum and in kidney biopsies of the same patient at the time of transplantation and at the time of disease recurrence. The analysis revealed changes in podocyte metabolome induced by the FSGS serum as well as in focal glomerular and parietal epithelial cell regions in the FSGS biopsy. Several altered Raman spectra were identified in the fractionated serum and metabolome analysis by mass spectrometry detected lipid profiles in the FSGS serum, which were supported by disturbances in the Raman spectra. Our novel innovative analysis reveals changed lipid metabolome profiles associated with idiopathic FSGS that might reflect a new subtype of the disease.
    Type of Medium: Online Resource
    ISSN: 2045-2322
    URL: Article
    DOI: 10.1038/s41598-021-83883-w
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2021
    detail.hit.zdb_id: 2615211-3
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