Dr. Roland Opfer Profile

Dr. Roland Opfer

at jung diagnostics GmbH

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Author

Publications (16)

Proceedings Article | 3 April 2024 Poster + Paper

Quantitative evaluation of activation maps for weakly-supervised lung nodule segmentation

Finn Behrendt, Suyash Sonawane, Debayan Bhattacharya, Lennart Maack, Julia Krüger, Roland Opfer, Alexander Schlaefer

Proceedings Volume 12927, 129272P (2024) https://doi.org/10.1117/12.3006416

KEYWORDS: Image segmentation, Education and training, Chest imaging, Visualization, Deep learning, Performance modeling, Pathology, Lung, Convolutional neural networks, Artificial intelligence

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Proceedings Article | 3 April 2024 Poster + Paper

Focused unsupervised image registration for structure-specific population analysis

Jan Ehrhardt, Hristina Uzunova, Paul Kaftan, Julia Krüger, Roland Opfer, Heinz Handels

Proceedings Volume 12927, 129272F (2024) https://doi.org/10.1117/12.3006119

KEYWORDS: Image registration, Image segmentation, Deformation, Image resolution, Image processing, Neuroimaging, Brain, Alzheimer disease

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Proceedings Article | 4 April 2022 Presentation + Paper

Unsupervised anomaly detection in 3D brain MRI using deep learning with multi-task brain age prediction

Marcel Bengs, Finn Behrendt, Max-Heinrich Laves, Julia Krüger, Roland Opfer, Alexander Schlaefer

Proceedings Volume 12033, 1203314 (2022) https://doi.org/10.1117/12.2608120

KEYWORDS: Computer aided diagnosis and therapy, Machine learning, Computer vision technology, Pattern recognition, Brain, Magnetic resonance imaging, Neuroimaging, Magnetism, Intelligence systems, Diagnostics, Computer programming, 3D scanning, 3D magnetic resonance imaging

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Proceedings Article | 18 March 2010 Paper

Towards automatic determination of total tumor burden from PET images

Steffen Renisch, Roland Opfer, Rafael Wiemker

Proceedings Volume 7624, 76241T (2010) https://doi.org/10.1117/12.844225

KEYWORDS: Positron emission tomography, Tumors, Cancer, Image segmentation, Acquisition tracking and pointing, Brain, Lung cancer, Detection and tracking algorithms, Lymphoma, Computed tomography

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Proceedings Article | 3 March 2009 Paper

Follow-up segmentation of lung tumors in PET and CT data

Roland Opfer, Sven Kabus, Torben Schneider, Ingwer Carlsen, Steffen Renisch, Jörg Sabczynski

Proceedings Volume 7260, 72600X (2009) https://doi.org/10.1117/12.811599

KEYWORDS: Tumors, Computed tomography, Image segmentation, Positron emission tomography, Lung, Lung cancer, Cancer, Optical spheres, Radiotherapy, Data acquisition

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Proceedings Article | 24 March 2008 Paper

Performance study of a globally elastic locally rigid matching algorithm for follow-up chest CT

Rafael Wiemker, Bartjan de Hoop, Sven Kabus, Hester Gietema, Roland Opfer, Ekta Dharaiya

Proceedings Volume 6917, 691706 (2008) https://doi.org/10.1117/12.765166

KEYWORDS: Lung, Computed tomography, Chest, Image registration, Image segmentation, Collimation, Computer simulations, Medical imaging, Detection and tracking algorithms, Computer aided diagnosis and therapy

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Proceedings Article | 17 March 2008 Paper

Automatic lesion tracking for a PET/CT based computer aided cancer therapy monitoring system

Roland Opfer, Winfried Brenner, Ingwer Carlsen, Steffen Renisch, Jörg Sabczynski, Rafael Wiemker

Proceedings Volume 6915, 691513 (2008) https://doi.org/10.1117/12.770356

KEYWORDS: Cancer, Positron emission tomography, Detection and tracking algorithms, Computed tomography, Rigid registration, Tumors, Computing systems, Automatic tracking, Nuclear medicine, Image registration

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Proceedings Article | 17 March 2008 Paper

Comparison of computer-aided diagnosis performance and radiologist readings on the LIDC pulmonary nodule dataset

Luyin Zhao, Michael Lee, Lilla Boroczky, Victor Vloemans, Roland Opfer

Proceedings Volume 6915, 69151I (2008) https://doi.org/10.1117/12.769481

KEYWORDS: Computer aided diagnosis and therapy, Calibration, Image segmentation, Computed tomography, Binary data, Databases, Statistical analysis, Image processing, Machine learning, Computing systems

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Proceedings Article | 17 March 2008 Paper

Repeatability and noise robustness of spicularity features for computer aided characterization of pulmonary nodules in CT

Rafael Wiemker, Roland Opfer, Thomas Bülow, Sven Kabus, Ekta Dharaiya

Proceedings Volume 6915, 691511 (2008) https://doi.org/10.1117/12.765132

KEYWORDS: Computed tomography, Image segmentation, Computer aided diagnosis and therapy, Lung, Image processing, Solids, Natural surfaces, Diagnostics, Medical imaging, Tumors

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Computer aided characterization aims to support the differential diagnosis of indeterminate pulmonary nodules. A number of published studies have correlated automatically computed features from image processing with clinical diagnoses of malignancy vs. benignity. Often, however, a high number of features was trained on a relatively small number of diagnosed nodules, raising a certain skepticism as to how salient and numerically robust the various features really are. On the way towards computer aided diagnosis which is trusted in clinical practice, the credibility of the individual numerical features has to be carefully established. Nodule volume is the most crucial parameter for nodule characterization, and a number of studies are testing its repeatability. Apart from functional parameters (such as dynamic CT enhancement and PET uptake values), the next most widely used parameter is the surface characteristic (vascularization, spicularity, lobulation, smoothness). In this study, we test the repeatability of two simple surface smoothness features which can discriminate between smoothly delineated nodules and those with a high degree of surface irregularity. Robustness of the completely automatically computed features was tested with respect to the following aspects: (a) repeated CT scan of the same patient with equal dose, (b) repeated CT scan with much lower dose and much higher noise, (c) repeated automatic segmentation of the nodules using varying segmentation parameters, resulting in differing nodule surfaces. The tested nodules (81) were all solid or partially solid and included a high number of sub- and juxtapleural nodules. We found that both tested surface characterization features correlated reasonably well with each other (80%), and that in particular the mean-surface-shape-index showed an excellent repeatability: 98% correlation between equal dose CT scans, 93% between standard-dose and low-dose scan (without systematic shift), and 97% between varying HU-threshold of the automatic segmentation, which makes it a reliable feature to be used in computer aided diagnosis.

Proceedings Article | 12 March 2008 Paper

Intuitive parameter-free visualization of tumor vascularization using rotating connectivity projections

Rafael Wiemker, Thomas Bülow, Roland Opfer, Sven Kabus, Ekta Dharaiya

Proceedings Volume 6916, 69161Z (2008) https://doi.org/10.1117/12.765333

KEYWORDS: Tumors, Visualization, Image segmentation, Lung, Computed tomography, Distortion, Binary data, Medical imaging, Image visualization, Optical inspection

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Proceedings Article | 29 March 2007 Paper

Automated hierarchical partitioning of anatomical trees

Rafael Wiemker, Thomas Bülow, Roland Opfer

Proceedings Volume 6511, 65110G (2007) https://doi.org/10.1117/12.705854

KEYWORDS: Image segmentation, 3D image processing, Image filtering, Medical imaging, Image processing algorithms and systems, Lung, Image processing, X-ray computed tomography, Magnetic resonance imaging, X-ray imaging

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Proceedings Article | 29 March 2007 Paper

Toward computer-aided emphysema quantification on ultralow-dose CT: reproducibility of ventrodorsal gravity effect measurement and correction

Rafael Wiemker, Roland Opfer, Thomas Bülow, Patrik Rogalla, Amnon Steinberg, Ekta Dharaiya, Krishna Subramanyan

Proceedings Volume 6514, 65140O (2007) https://doi.org/10.1117/12.705868

KEYWORDS: Lung, Emphysema, Computed tomography, X-ray computed tomography, Signal attenuation, Tissues, Image segmentation, X-rays, Statistical analysis, Computer aided diagnosis and therapy

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Proceedings Article | 20 March 2007 Paper

Performance analysis for computer-aided lung nodule detection on LIDC data

Roland Opfer, Rafael Wiemker

Proceedings Volume 6515, 65151C (2007) https://doi.org/10.1117/12.708210

KEYWORDS: Computer aided diagnosis and therapy, CAD systems, Lung, Lung cancer, Databases, Medical imaging, Receivers, Cancer, Spherical lenses, Analytical research

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Proceedings Article | 3 March 2007 Paper

A new general tumor segmentation framework based on radial basis function energy minimization with a validation study on LIDC lung nodules

Roland Opfer, Rafael Wiemker

Proceedings Volume 6512, 651217 (2007) https://doi.org/10.1117/12.708183

KEYWORDS: Image segmentation, Tumors, Lung, 3D image processing, Optical spheres, Computed tomography, Databases, Image processing algorithms and systems, Lung cancer, Surgery

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Proceedings Article | 17 March 2006 Paper

Comparative performance analysis for computer-aided lung nodule detection and segmentation on ultra-low-dose vs. standard dose CT

Rafael Wiemker, Patrik Rogalla, Roland Opfer, Ahmet Ekin, Valentina Romano, Thomas Bülow

Proceedings Volume 6146, 614605 (2006) https://doi.org/10.1117/12.649790

KEYWORDS: Computer aided diagnosis and therapy, Computed tomography, Lung, Lung cancer, Detection and tracking algorithms, Computer aided design, Scanners, Visualization, X-ray computed tomography, Feature extraction

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Proceedings Article | 13 March 2006 Paper

Unsupervised extraction and quantification of the bronchial tree on ultra-low-dose vs. standard-dose CT

Rafael Wiemker, Ahmet Ekin, Roland Opfer, Thomas Bülow, Patrik Rogalla

Proceedings Volume 6143, 61432V (2006) https://doi.org/10.1117/12.649530

KEYWORDS: Computed tomography, Optical spheres, X-ray computed tomography, Lung, Scanners, Visualization, 3D image processing, Wavefronts, Wave propagation, Optical inspection

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Showing 5 of 16 publications

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