Mr. Pieter C. Vos Profile

Pieter C. Vos

Postdoc researcher at Image Sciences Institute

SPIE Involvement:

Author

Area of Expertise:

Prostate , Perfusion , CT , Cancer , MRI , Stroke

Websites:

Company Website

Publications (8)

Proceedings Article | 20 March 2015 Paper

Automated prediction of tissue outcome after acute ischemic stroke in computed tomography perfusion images

Pieter Vos, Edwin Bennink, Hugo de Jong, Birgitta Velthuis, Max Viergever, Jan Willem Dankbaar

Proceedings Volume 9414, 941412 (2015) https://doi.org/10.1117/12.2081600

KEYWORDS: Tissues, Computed tomography, Image segmentation, Ischemic stroke, Brain, Neuroimaging, Visualization, Image processing, Receivers, Blood

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Proceedings Article | 29 March 2013 Paper

Automatic detection and segmentation of ischemic lesions in computed tomography images of stroke patients

Birgitta Velthuis, Max Viergever, Pieter Vos, J. Matthijs Biesbroek, Nick Weaver

Proceedings Volume 8670, 867013 (2013) https://doi.org/10.1117/12.2008074

KEYWORDS: Image segmentation, Computed tomography, Feature extraction, Tissues, Statistical analysis, Ischemia, Clinical trials, Receivers, Feature selection, Medicine

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Proceedings Article | 13 March 2013 Paper

Combined pixel classification and atlas-based segmentation of the ventricular system in brain CT Images

Ivana Išgum, Birgitta Velthuis, Max Viergever, Pieter Vos, J. Matthijs Biesbroek

Proceedings Volume 8669, 86691O (2013) https://doi.org/10.1117/12.2006222

KEYWORDS: Image segmentation, Computed tomography, Brain, Neuroimaging, Image registration, Binary data, Imaging systems, Classification systems, Computing systems, Ischemic stroke

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Proceedings Article | 28 February 2013 Paper

Computer-aided diagnosis of acute ischemic stroke based on cerebral hypoperfusion using 4D CT angiography

Jean-Paul Charbonnier, Ewoud Smit, Max Viergever, Birgitta Velthuis, Pieter Vos

Proceedings Volume 8670, 867012 (2013) https://doi.org/10.1117/12.2007735

KEYWORDS: Computer aided diagnosis and therapy, Blood, Ischemic stroke, Angiography, Computer aided design, Brain, CAD systems, Computed tomography, Feature extraction, Arteries

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Proceedings Article | 4 March 2011 Paper

Automatic computer aided detection of abnormalities in multi-parametric prostate MRI

J. Barentsz, H. Huisman, P. Vos, G. J. Litjens, N. Karssemeijer

Proceedings Volume 7963, 79630T (2011) https://doi.org/10.1117/12.877844

KEYWORDS: Prostate, Magnetic resonance imaging, Prostate cancer, Image segmentation, Computer aided diagnosis and therapy, CAD systems, Sensors, Biopsy, Diffusion, Classification systems

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Proceedings Article | 9 March 2010 Paper

Automated segmentation of reference tissue for prostate cancer localization in dynamic contrast enhanced MRI

Thomas Hambrock, Henkjan Huisman, Pieter Vos, Jelle Barentsz

Proceedings Volume 7624, 76240F (2010) https://doi.org/10.1117/12.845501

KEYWORDS: Tissues, Calibration, Prostate, Magnetic resonance imaging, Image segmentation, Diagnostics, Computer aided diagnosis and therapy, Plasma, Tumors, Prostate cancer

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For pharmacokinetic (PK) analysis of Dynamic Contrast Enhanced (DCE) MRI the arterial input function needs to be estimated. Previously, we demonstrated that PK parameters have a significant better discriminative performance when per patient reference tissue was used, but required manual annotation of reference tissue. In this study we propose a fully automated reference tissue segmentation method that tackles this limitation. The method was tested with our Computer Aided Diagnosis (CADx) system to study the effect on the discriminating performance for differentiating prostate cancer from benign areas in the peripheral zone (PZ). The proposed method automatically segments normal PZ tissue from DCE derived data. First, the bladder is segmented in the start-to-enhance map using the Otsu histogram threshold selection method. Second, the prostate is detected by applying a multi-scale Hessian filter to the relative enhancement map. Third, normal PZ tissue was segmented by threshold and morphological operators. The resulting segmentation was used as reference tissue to estimate the PK parameters. In 39 consecutive patients carcinoma, benign and normal tissue were annotated on MR images by a radiologist and a researcher using whole mount step-section histopathology as reference. PK parameters were computed for each ROI. Features were extracted from the set of ROIs using percentiles to train a support vector machine that was used as classifier. Prospective performance was estimated by means of leave-one-patient-out cross validation. A bootstrap resampling approach with 10,000 iterations was used for estimating the bootstrap mean AUCs and 95% confidence intervals. In total 42 malignant, 29 benign and 37 normal regions were annotated. For all patients, normal PZ was successfully segmented. The diagnostic accuracy obtained for differentiating malignant from benign lesions using a conventional general patient plasma profile showed an accuracy of 0.64 (0.53-0.74). Using the automated per-patient calibration method the diagnostic performance improved significantly to 0.76 (0.67-0.86, p=0.017) , whereas the manual per-patient calibration showed a diagnostic performance of 0.79 (0.70-0.89, p=0.01). In conclusion, the results show that an automated per-patient reference tissue PK model is feasible. A significantly better discriminating performance compared to the conventional general calibration was obtained and the diagnostic accuracy is similar to using manual per-patient calibration.

Proceedings Article | 17 March 2008 Paper

Combining T2-weighted with dynamic MR images for computerized classification of prostate lesions

Henkjan Huisman, Jelle Barentsz, Thomas Hambrock, Pieter Vos

Proceedings Volume 6915, 69150W (2008) https://doi.org/10.1117/12.771970

KEYWORDS: Diagnostics, Magnetic resonance imaging, CAD systems, Prostate, Image registration, Tumors, Tissues, Principal component analysis, Prostate cancer, Image classification

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Proceedings Article | 29 March 2007 Paper

Effect of calibration on computerized analysis of prostate lesions using quantitative dynamic contrast-enhanced magnetic resonance imaging

Jurgen Fütterer, Henkjan Huisman, C. Hulsbergen-van de Kaa, Pieter Vos, Thomas Hambrock, Jelle Barentsz

Proceedings Volume 6514, 65140U (2007) https://doi.org/10.1117/12.709357

KEYWORDS: Calibration, Prostate, Magnetic resonance imaging, CAD systems, Tissues, Diagnostics, Plasma, Principal component analysis, Computer aided diagnosis and therapy, Databases

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Showing 5 of 8 publications

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