Prof. Nico Karssemeijer Profile

Prof. Nico Karssemeijer

SPIE Involvement:

Author | Instructor

Publications (66)

SPIE Journal Paper | 29 September 2023

Robust cross-vendor mammographic texture models using augmentation-based domain adaptation for long-term breast cancer risk

Andreas D. Lauritzen, My Catarina von Euler-Chelpin, Elsebeth Lynge, Ilse Vejborg, Mads Nielsen, Nico Karssemeijer, Martin Lillholm

JMI, Vol. 10, Issue 05, 054003, (September 2023) https://doi.org/10.1117/12.10.1117/1.JMI.10.5.054003

KEYWORDS: Education and training, Data modeling, Mammography, Breast cancer, Cancer, Tumor growth modeling, Breast, Tunable filters, Instrument modeling, Cancer detection

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Proceedings Article | 22 May 2020 Paper

Evaluation of elastic parameters for breast compression using a MRI-mammography registration approach

E. García, Y. Diez, A. Oliver, N. Karssemeijer, J. Martí, R. Martí, O. Diaz

Proceedings Volume 11513, 1151306 (2020) https://doi.org/10.1117/12.2564155

KEYWORDS: Tissues, Breast, Magnetic resonance imaging, Magnesium, Mammography, Image processing, Image registration, Image compression, Image segmentation, X-ray imaging

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Proceedings Article | 22 May 2020 Paper

Going from double to single reading for screening exams labeled as likely normal by AI: what is the impact?

Christiana Balta, Alejandro Rodriguez-Ruiz, Christoph Mieskes, Nico Karssemeijer, Sylvia Heywang-Köbrunner

Proceedings Volume 11513, 115130D (2020) https://doi.org/10.1117/12.2564179

KEYWORDS: Cancer, Artificial intelligence, Mammography, Breast cancer, Breast, Intelligence systems, Computer-aided diagnosis, Computing systems, Data acquisition, Data processing

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SPIE Journal Paper | 27 September 2019

Optimization of the difference-of-Gaussian channel sets for the channelized Hotelling observer

Christiana Balta, Ramona Bouwman, Mireille J. Broeders, Nico Karssemeijer, Wouter J. Veldkamp, Ioannis Sechopoulos, Ruben van Engen

JMI, Vol. 6, Issue 03, 035501, (September 2019) https://doi.org/10.1117/12.10.1117/1.JMI.6.3.035501

KEYWORDS: Signal detection, Image quality, Breast, Image processing, Image analysis, Medical imaging, Digital mammography, Digital image processing, Gold, Radiology

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Proceedings Article | 13 March 2019 Presentation + Paper

A deep learning method for volumetric breast density estimation from processed full field digital mammograms

Doiriel Vanegas C., Mahlet Birhanu, Nico Karssemeijer, Albert Gubern-Mérida, Michiel Kallenberg

Proceedings Volume 10950, 109500F (2019) https://doi.org/10.1117/12.2512818

KEYWORDS: Mammography, Breast, Image processing, Magnetic resonance imaging, Breast cancer, Convolutional neural networks, Digital imaging, Convolution, Classification systems

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Proceedings Article | 4 March 2019 Presentation + Paper

New difference of Gaussian channel-sets for the channelized Hotelling observer?

Christiana Balta, Ioannis Sechopoulos, Ramona Bouwman, Mireille J. Broeders, Nico Karssemeijer, Ruben van Engen, Wouter J. Veldkamp

Proceedings Volume 10952, 109520C (2019) https://doi.org/10.1117/12.2512054

KEYWORDS: Signal detection, Breast, Image quality, Gold, Radiology, Digital mammography, Digital imaging, Aluminum, Nuclear medicine, Mammography

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Proceedings Article | 6 July 2018 Paper

Mammogram denoising to improve the calcification detection performance of convolutional nets

Claudio Marrocco, Alessandro Bria, Valerio Di Sano, Lucas Borges, Benedetta Savelli, Mario Molinara, Jan-Jurre Mordang, Nico Karssemeijer, Francesco Tortorella

Proceedings Volume 10718, 107180W (2018) https://doi.org/10.1117/12.2318069

KEYWORDS: Mammography, Denoising, Signal to noise ratio, Image filtering, Breast, Convolutional neural networks

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Proceedings Article | 6 July 2018 Paper

Improving the automated detection of calcifications by combining deep cascades and deep convolutional nets

Alessandro Bria, Claudio Marrocco, Mario Molinara, Benedetta Savelli, Jan-Jurre Mordang, Nico Karssemeijer, Francesco Tortorella

Proceedings Volume 10718, 1071808 (2018) https://doi.org/10.1117/12.2318016

KEYWORDS: Mammography, Image classification, Convolutional neural networks, Computer aided design, Classification systems, Digital mammography

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Proceedings Article | 6 July 2018 Paper

How does wide-angle breast tomosynthesis depict calcifications in comparison to digital mammography? A retrospective observer study

Alejandro Rodriguez-Ruiz, Ruben van Engen, Koen Michielsen, Ramona Bouwman, Suzan Vreemann, Nico Karssemeijer, Ritse Mann, Ioannis Sechopoulos

Proceedings Volume 10718, 107181T (2018) https://doi.org/10.1117/12.2318054

KEYWORDS: Digital breast tomosynthesis, Breast, Visualization, Digital mammography, Neodymium, Mammography, Reconstruction algorithms, Breast cancer, Breast imaging, Sensors

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Proceedings Article | 6 July 2018 Paper

Can radiologists improve their breast cancer detection in mammography when using a deep learning based computer system as decision support?

Alejandro Rodriguez-Ruiz, Jan-Jurre Mordang, Nico Karssemeijer, Ioannis Sechopoulos, Ritse Mann

Proceedings Volume 10718, 1071803 (2018) https://doi.org/10.1117/12.2317937

KEYWORDS: Computing systems, Mammography, Decision support systems, Cancer, Breast cancer, Statistical analysis, Machine learning, Evolutionary algorithms, Receivers, Tumor growth modeling

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Proceedings Article | 6 March 2018 Paper

H and E stain augmentation improves generalization of convolutional networks for histopathological mitosis detection

David Tellez, Maschenka Balkenhol, Nico Karssemeijer, Geert Litjens, Jeroen van der Laak, Francesco Ciompi

Proceedings Volume 10581, 105810Z (2018) https://doi.org/10.1117/12.2293048

KEYWORDS: Machine learning, Pathology, Data modeling, Breast cancer, Convolutional neural networks

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Proceedings Article | 2 March 2018 Paper

Student beats the teacher: deep neural networks for lateral ventricles segmentation in brain MR

Mohsen Ghafoorian, Jonas Teuwen, Rashindra Manniesing, Frank-Erik Leeuw, Bram van Ginneken, Nico Karssemeijer, Bram Platel

Proceedings Volume 10574, 105742U (2018) https://doi.org/10.1117/12.2293569

KEYWORDS: Image segmentation, Neural networks, Brain, Magnetic resonance imaging, Neuroimaging, Medical imaging, Image processing algorithms and systems, Network architectures, Brain diseases, Dementia

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Proceedings Article | 27 February 2018 Paper

Pectoral muscle segmentation in breast tomosynthesis with deep learning

Alejandro Rodriguez-Ruiz, Jonas Teuwen, Kaman Chung, Nico Karssemeijer, Margarita Chevalier, Albert Gubern-Merida, Ioannis Sechopoulos

Proceedings Volume 10575, 105752J (2018) https://doi.org/10.1117/12.2292920

KEYWORDS: Image segmentation, Digital breast tomosynthesis, Breast, Mammography, Systems modeling, Detection and tracking algorithms, Tissues, Convolutional neural networks, 3D modeling, Image processing algorithms and systems

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SPIE Journal Paper | 11 January 2018 Open Access

Fully automated detection of breast cancer in screening MRI using convolutional neural networks

Mehmet Ufuk Dalmış, Suzan Vreemann, Thijs Kooi, Ritse M. Mann, Nico Karssemeijer, Albert Gubern-Mérida

JMI, Vol. 5, Issue 01, 014502, (January 2018) https://doi.org/10.1117/12.10.1117/1.JMI.5.1.014502

KEYWORDS: Magnetic resonance imaging, Breast, Computer aided diagnosis and therapy, Breast cancer, Image segmentation, Convolutional neural networks, Computing systems, 3D acquisition, Cancer

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SPIE Journal Paper | 14 December 2017

Context-aware stacked convolutional neural networks for classification of breast carcinomas in whole-slide histopathology images

Babak Ehteshami Bejnordi, Guido Zuidhof, Maschenka Balkenhol, Meyke Hermsen, Peter Bult, Bram van Ginneken, Nico Karssemeijer, Geert Litjens, Jeroen van der Laak

JMI, Vol. 4, Issue 04, 044504, (December 2017) https://doi.org/10.1117/12.10.1117/1.JMI.4.4.044504

KEYWORDS: Binary data, Breast, Tissues, Image classification, Convolutional neural networks, Classification systems, Cancer, Diagnostics, Breast cancer, RGB color model

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SPIE Journal Paper | 10 October 2017

Classifying symmetrical differences and temporal change for the detection of malignant masses in mammography using deep neural networks

Thijs Kooi, Nico Karssemeijer

JMI, Vol. 4, Issue 04, 044501, (October 2017) https://doi.org/10.1117/12.10.1117/1.JMI.4.4.044501

KEYWORDS: Mammography, Breast, Data modeling, Neural networks, Tissues, Computer aided diagnosis and therapy, Image fusion, Medical imaging, Convolutional neural networks, Associative arrays

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Proceedings Article | 10 March 2017 Presentation + Paper

Signal template generation from acquired mammographic images for the non-prewhitening model observer with eye-filter

Christiana Balta, Ramona Bouwman, Ioannis Sechopoulos, Mireille J. M. Broeders, Nico Karssemeijer, Ruben van Engen, Wouter J. H. Veldkamp

Proceedings Volume 10136, 101360M (2017) https://doi.org/10.1117/12.2254160

KEYWORDS: Eye models, Breast, Image quality, Image filtering, Digital mammography, Signal detection, Aluminum, Gold

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Proceedings Article | 3 March 2017 Presentation + Paper

Deep learning of symmetrical discrepancies for computer-aided detection of mammographic masses

Thijs Kooi, Nico Karssemeijer

Proceedings Volume 10134, 101341J (2017) https://doi.org/10.1117/12.2254586

KEYWORDS: Convolutional neural networks, Mammography, Medical imaging, Computer-aided diagnosis, Neural networks, Image analysis, Deep learning, Breast, Image fusion, Tissues, Nipple

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Proceedings Article | 3 March 2017 Presentation + Paper

Conditional random field modelling of interactions between findings in mammography

Thijs Kooi, Jan-Jurre Mordang, Nico Karssemeijer

Proceedings Volume 10134, 101341E (2017) https://doi.org/10.1117/12.2254133

KEYWORDS: Mammography, Computer aided diagnosis and therapy, Neural networks, Medical imaging, Machine learning, Breast, Deep learning, Convolutional neural networks, Tumor growth modeling, Sensors, Tissues, Systems modeling

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Proceedings Article | 24 March 2016 Paper

Quantification of mammographic masking risk with volumetric breast density maps: how to select women for supplemental screening

Katharina Holland, Carla van Gils, Johanna OP Wanders, Ritse Mann, Nico Karssemeijer

Proceedings Volume 9785, 97850I (2016) https://doi.org/10.1117/12.2216810

KEYWORDS: Cancer, Breast imaging, Ultrasonography, Breast, Mammography, Breast cancer, Tumors, Magnetic resonance imaging

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Proceedings Article | 24 March 2016 Paper

Automated linking of suspicious findings between automated 3D breast ultrasound volumes

Albert Gubern-Mérida, Tao Tan, Jan van Zelst, Ritse Mann, Nico Karssemeijer

Proceedings Volume 9785, 97850N (2016) https://doi.org/10.1117/12.2214945

KEYWORDS: Breast, Ultrasonography, 3D image processing, 3D displays, Detection and tracking algorithms, Computer-aided diagnosis, Breast cancer, Image analysis, Inspection, Decision support systems, Clocks, Picosecond phenomena, Nipple, Chest, Error analysis

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Proceedings Article | 24 March 2016 Paper

A fully automated system for quantification of background parenchymal enhancement in breast DCE-MRI

Mehmet Ufuk Dalmiş, Albert Gubern-Mérida, Cristina Borelli, Suzan Vreemann, Ritse Mann, Nico Karssemeijer

Proceedings Volume 9785, 97850L (2016) https://doi.org/10.1117/12.2211640

KEYWORDS: Breast, Breast cancer, Image segmentation, Magnetic resonance imaging, Visualization, Expectation maximization algorithms, Optical inspection, Imaging systems

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Proceedings Article | 20 March 2015 Paper

Small white matter lesion detection in cerebral small vessel disease

Mohsen Ghafoorian, Nico Karssemeijer, Inge van Uden, Frank de Leeuw, Tom Heskes, Elena Marchiori, Bram Platel

Proceedings Volume 9414, 941411 (2015) https://doi.org/10.1117/12.2081597

KEYWORDS: Brain, Image segmentation, Tissues, Magnetic resonance imaging, Neuroimaging, CAD systems, Medical imaging, Magnetism, Alzheimer's disease, Dementia

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Proceedings Article | 20 March 2015 Paper

Vessel segmentation in screening mammograms

J .J. Mordang, N. Karssemeijer

Proceedings Volume 9414, 94140J (2015) https://doi.org/10.1117/12.2081804

KEYWORDS: Mammography, Image segmentation, Blood vessels, Breast cancer, Computer aided diagnosis and therapy, Receivers, Breast, Computing systems, Gaussian filters, Image analysis

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Proceedings Article | 19 March 2015 Paper

A multi-scale superpixel classification approach to the detection of regions of interest in whole slide histopathology images

Babak Bejnordi, Geert Litjens, Meyke Hermsen, Nico Karssemeijer, Jeroen A. W. van der Laak

Proceedings Volume 9420, 94200H (2015) https://doi.org/10.1117/12.2081768

KEYWORDS: Tissues, Image classification, Detection and tracking algorithms, Breast, Image analysis, Image processing, Visualization, Breast cancer, Image segmentation, Image processing algorithms and systems

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SPIE Journal Paper | 25 July 2014

Incorporating texture features in a computer-aided breast lesion diagnosis system for automated three-dimensional breast ultrasound

Haixia Liu, Tao Tan, Jan van Zelst, Ritse Mann, Nico Karssemeijer, Bram Platel

JMI, Vol. 1, Issue 02, 024501, (July 2014) https://doi.org/10.1117/12.10.1117/1.JMI.1.2.024501

KEYWORDS: Breast, Image classification, CAD systems, Computing systems, Feature extraction, Computer aided diagnosis and therapy, Optical fiber cables, Ultrasonography, 3D image processing, Cancer

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SPIE Journal Paper | 23 April 2014 Open Access

Influence of study design in receiver operating characteristics studies: sequential versus independent reading

Steven Schalekamp, Bram van Ginneken, Cornelia Schaefer-Prokop, Nico Karssemeijer

JMI, Vol. 1, Issue 01, 015501, (April 2014) https://doi.org/10.1117/12.10.1117/1.JMI.1.1.015501

KEYWORDS: Computer aided design, Radiography, Receivers, Chest imaging, Statistical analysis, Lung, Computer aided diagnosis and therapy, Control systems, Image processing, Computed tomography

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Proceedings Article | 21 March 2014 Paper

Automatic nipple detection on 3D images of an automated breast ultrasound system (ABUS)

Mandana Javanshir Moghaddam, Tao Tan, Nico Karssemeijer, Bram Platel

Proceedings Volume 9034, 903405 (2014) https://doi.org/10.1117/12.2043780

KEYWORDS: Nipple, Breast, Ultrasonography, Tissues, Mammography, 3D image processing, Breast cancer, 3D acquisition, Hough transforms, Feature extraction

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Proceedings Article | 20 March 2014 Paper

Boosting classification performance in computer aided diagnosis of breast masses in raw full-field digital mammography using processed and screen film images

Thijs Kooi, Nico Karssemeijer

Proceedings Volume 9035, 90351B (2014) https://doi.org/10.1117/12.2042863

KEYWORDS: Image processing, Atomic force microscopy, Computer aided diagnosis and therapy, Data processing, Digital mammography, Breast, CAD systems, Image segmentation, Digital image processing, Mammography

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Proceedings Article | 20 March 2014 Paper

Chest-wall segmentation in automated 3D breast ultrasound images using thoracic volume classification

Tao Tan, Jan van Zelst, Wei Zhang, Ritse Mann, Bram Platel, Nico Karssemeijer

Proceedings Volume 9035, 90351Y (2014) https://doi.org/10.1117/12.2043552

KEYWORDS: Image segmentation, Breast, Ultrasonography, 3D image processing, Chest, Computer aided diagnosis and therapy, Tissues, 3D modeling, Image classification, Cancer

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Proceedings Article | 20 March 2014 Paper

Quantitative analysis of stain variability in histology slides and an algorithm for standardization

Babak Ehteshami Bejnordi, Nadya Timofeeva, Irene Otte-Höller, Nico Karssemeijer, Jeroen A. W. van der Laak

Proceedings Volume 9041, 904108 (2014) https://doi.org/10.1117/12.2043683

KEYWORDS: Tissues, RGB color model, Data modeling, Image segmentation, Image processing algorithms and systems, Expectation maximization algorithms, Quantitative analysis, Absorption, Pathology, Transform theory

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Proceedings Article | 28 March 2013 Paper

Impact of bone suppression imaging on the detection of lung nodules in chest radiographs: analysis of multiple reading sessions

S. Schalekamp, B. van Ginneken, C. Schaefer-Prokop, N. Karssemeijer

Proceedings Volume 8673, 86730Y (2013) https://doi.org/10.1117/12.2002799

KEYWORDS: Chest imaging, Bone, Lung, Radiography, Computer aided design, Receivers, Computer aided diagnosis and therapy, Image processing, Control systems, Computed tomography

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Proceedings Article | 15 March 2013 Paper

Surface-based prostate registration with biomechanical regularization

Wendy J.M. van de Ven, Yipeng Hu, Jelle Barentsz, Nico Karssemeijer, Dean Barratt, Henkjan Huisman

Proceedings Volume 8671, 86711R (2013) https://doi.org/10.1117/12.2007580

KEYWORDS: Prostate, Image registration, Magnetic resonance imaging, Biopsy, Image segmentation, Ultrasonography, Tumors, Electroluminescent displays, Prostate cancer, 3D image processing

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Proceedings Article | 13 March 2013 Paper

Breast segmentation in MRI: quantitative evaluation of three methods

Albert Gubern-Mérida, Lei Wang, Michiel Kallenberg, Robert Martí, Horst Hahn, Nico Karssemeijer

Proceedings Volume 8669, 86693G (2013) https://doi.org/10.1117/12.2006541

KEYWORDS: Breast, Image segmentation, Tissues, Magnetic resonance imaging, Natural surfaces, Computer aided diagnosis and therapy, Image filtering, Distance measurement, Breast cancer, 3D image processing

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Proceedings Article | 28 February 2013 Paper

Finding lesion correspondences in different views of automated 3D breast ultrasound

Tao Tan, Bram Platel, Michael Hicks, Ritse Mann, Nico Karssemeijer

Proceedings Volume 8670, 86701N (2013) https://doi.org/10.1117/12.2007475

KEYWORDS: Breast, Nipple, Ultrasonography, Transducers, 3D acquisition, Computer aided diagnosis and therapy, Mammography, 3D vision, Cancer, Tissues

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Proceedings Article | 27 February 2013 Paper

Detection of white matter lesions in cerebral small vessel disease

Medhat Riad, Bram Platel, Frank-Erik de Leeuw, Nico Karssemeijer

Proceedings Volume 8670, 867014 (2013) https://doi.org/10.1117/12.2007940

KEYWORDS: Brain, Magnetic resonance imaging, Image segmentation, Tissues, Image classification, Dementia, Neurology, Feature extraction, Analytical research, Computer aided diagnosis and therapy

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White matter lesions (WML) are diffuse white matter abnormalities commonly found in older subjects and are important indicators of stroke, multiple sclerosis, dementia and other disorders. We present an automated WML detection method and evaluate it on a dataset of small vessel disease (SVD) patients. In early SVD, small WMLs are expected to be of importance for the prediction of disease progression. Commonly used WML segmentation methods tend to ignore small WMLs and are mostly validated on the basis of total lesion load or a Dice coefficient for all detected WMLs. Therefore, in this paper, we present a method that is designed to detect individual lesions, large or small, and we validate the detection performance of our system with FROC (free-response ROC) analysis. For the automated detection, we use supervised classification making use of multimodal voxel based features from different magnetic resonance imaging (MRI) sequences, including intensities, tissue probabilities, voxel locations and distances, neighborhood textures and others. After preprocessing, including co-registration, brain extraction, bias correction, intensity normalization, and nonlinear registration, ventricle segmentation is performed and features are calculated for each brain voxel. A gentle-boost classifier is trained using these features from 50 manually annotated subjects to give each voxel a probability of being a lesion voxel. We perform ROC analysis to illustrate the benefits of using additional features to the commonly used voxel intensities; significantly increasing the area under the curve (A_z) from 0.81 to 0.96 (p<0.05). We perform the FROC analysis by testing our classifier on 50 previously unseen subjects and compare the results with manual annotations performed by two experts. Using the first annotator results as our reference, the second annotator performs at a sensitivity of 0.90 with an average of 41 false positives per subject while our automated method reached the same level of sensitivity at approximately 180 false positives per subject.

Proceedings Article | 23 February 2012 Paper

Detection of breast cancer in automated 3D breast ultrasound

Tao Tan, Bram Platel, Roel Mus, Nico Karssemeijer

Proceedings Volume 8315, 831505 (2012) https://doi.org/10.1117/12.911068

KEYWORDS: Breast, Ultrasonography, Image segmentation, Nipple, Breast cancer, Mammography, Computer aided diagnosis and therapy, Feature extraction, Cancer, 3D image processing

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Proceedings Article | 23 February 2012 Paper

Automated computer-aided detection of prostate cancer in MR images: from a whole-organ to a zone-based approach

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

Proceedings Volume 8315, 83150G (2012) https://doi.org/10.1117/12.911061

KEYWORDS: Image segmentation, Prostate, Computer aided design, Computer aided diagnosis and therapy, Blob detection, Prostate cancer, Cancer, Magnetic resonance imaging, CAD systems, Feature extraction

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Proceedings Article | 9 March 2011 Paper

Computer aided detection of breast masses in mammography using support vector machine classification

Jan Lesniak, Rianne Hupse, Michiel Kallenberg, Maurice Samulski, Rémi Blanc, Nico Karssemeijer, Gàbor Székely

Proceedings Volume 7963, 79631K (2011) https://doi.org/10.1117/12.878140

KEYWORDS: Breast, Mammography, Computer aided diagnosis and therapy, Neural networks, Image segmentation, Computer aided design, Databases, Receivers, Image classification, Cancer

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Proceedings Article | 4 March 2011 Paper

Classification of breast lesions in automated 3D breast ultrasound

Tao Tan, Henkjan Huisman, Bram Platel, Andre Grivegnee, Roel Mus, Nico Karssemeijer

Proceedings Volume 7963, 79630X (2011) https://doi.org/10.1117/12.877924

KEYWORDS: Breast, Ultrasonography, Feature selection, 3D image processing, Mammography, Cancer, Feature extraction, Breast cancer, Transducers, Acoustics

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Proceedings Article | 4 March 2011 Paper

Automatic breast density segmentation based on pixel classification

Michiel Kallenberg, Mariëtte Lokate, Carla van Gils, Nico Karssemeijer

Proceedings Volume 7963, 796307 (2011) https://doi.org/10.1117/12.877522

KEYWORDS: Breast, Mammography, Image segmentation, Tissues, Breast cancer, Image acquisition, Image classification, Image quality, Computer aided diagnosis and therapy, Feature extraction

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Proceedings Article | 4 March 2011 Paper

Estimating corresponding locations in ipsilateral breast tomosynthesis views

Guido van Schie, Christine Tanner, Nico Karssemeijer

Proceedings Volume 7963, 796306 (2011) https://doi.org/10.1117/12.878239

KEYWORDS: Breast, Optical spheres, Digital breast tomosynthesis, Nipple, Tissues, Computer aided diagnosis and therapy, Mammography, Mathematical modeling, Data modeling

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Proceedings Article | 4 March 2011 Paper

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

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

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 | 3 March 2011 Paper

Computer-aided detection as a decision assistant in chest radiography

Maurice Samulski, Peter Snoeren, Bram Platel, Bram van Ginneken, Laurens Hogeweg, Cornelia Schaefer-Prokop, Nico Karssemeijer

Proceedings Volume 7966, 796614 (2011) https://doi.org/10.1117/12.877968

KEYWORDS: Computer aided design, Computer aided diagnosis and therapy, Chest imaging, Chest, Radiography, CAD systems, Databases, Computing systems, Lung

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Background. Contrary to what may be expected, finding abnormalities in complex images like pulmonary nodules in chest radiographs is not dominated by time-consuming search strategies but by an almost immediate global interpretation. This was already known in the nineteen-seventies from experiments with briefly flashed chest radiographs. Later on, experiments with eye-trackers showed that abnormalities attracted the attention quite fast but often without further reader actions. Prolonging one's search seldom leads to newly found abnormalities and may even increase the chance of errors. The problem of reading chest radiographs is therefore not dominated by finding the abnormalities, but by interpreting them. Hypothesis. This suggests that readers could benefit from computer-aided detection (CAD) systems not so much by their ability to prompt potential abnormalities, but more from their ability to 'interpret' the potential abnormalities. In this paper, this hypothesis was investigated by an observer experiment. Experiment. In one condition, the traditional CAD condition, the most suspicious CAD locations were shown to the subjects, without telling them the levels of suspiciousness according to CAD. In the other condition, interactive CAD condition, levels of suspiciousness were given, but only when readers requested them at specified locations. These two conditions focus on decreasing search errors and decision errors, respectively. Results of reading without CAD were also recorded. Six subjects, all non-radiologists, read 223 chest radiographs in both conditions. CAD results were obtained from the OnGuard 5.0 system developed by Riverain Medical (Miamisburg, Ohio). Results. The observer data were analyzed by Location Response Operating Characteristic analysis (LROC). It was found that: 1) With the aid of CAD, the performance is significantly better than without CAD; 2) The performance with interactive CAD is significantly better than with traditional CAD at low false positive rates.

Proceedings Article | 9 March 2010 Paper

Automated classification of lymph nodes in USPIO-enhanced MR-images: a comparison of three segmentation methods

Oscar Debats, Nico Karssemeijer, Jelle Barentsz, Henkjan Huisman

Proceedings Volume 7624, 76240Q (2010) https://doi.org/10.1117/12.845640

KEYWORDS: Image segmentation, Lymphatic system, Magnetic resonance imaging, Optical spheres, Computer aided diagnosis and therapy, Tissues, CAD systems, Spherical lenses, Diagnostics, Image processing algorithms and systems

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Proceedings Article | 9 March 2010 Paper

Breast MRI intensity non-uniformity correction using mean-shift

Aliaksei Makarau, Henkjan Huisman, Roel Mus, Miranda Zijp, Nico Karssemeijer

Proceedings Volume 7624, 76242D (2010) https://doi.org/10.1117/12.845612

KEYWORDS: Tissues, Breast, Magnetic resonance imaging, Image segmentation, Nonuniformity corrections, Computer aided diagnosis and therapy, Analytical research, Brain, Breast cancer, Image filtering

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Proceedings Article | 13 March 2009 Paper

Analysis of probed regions in an interactive CAD system for the detection of masses in mammograms

M. Samulski, A. Hupse, C. Boetes, G. den Heeten, N. Karssemeijer

Proceedings Volume 7263, 726314 (2009) https://doi.org/10.1117/12.813391

KEYWORDS: Computer aided diagnosis and therapy, Mammography, Computer aided design, CAD systems, Cancer, Computing systems, Detection and tracking algorithms, Visualization, Visual process modeling, Breast cancer

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Proceedings Article | 3 March 2009 Paper

Noise model for microcalcification detection in reconstructed tomosynthesis slices

Guido van Schie, Nico Karssemeijer

Proceedings Volume 7260, 72600M (2009) https://doi.org/10.1117/12.810936

KEYWORDS: Reconstruction algorithms, Interference (communication), Breast, Data modeling, Sensors, Detection and tracking algorithms, Signal attenuation, Systems modeling, Tissues, Image analysis

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Proceedings Article | 3 March 2009 Paper

The use of contextual information for computer aided detection of masses in mammograms

Rianne Hupse, Nico Karssemeijer

Proceedings Volume 7260, 72600Q (2009) https://doi.org/10.1117/12.812233

KEYWORDS: Mammography, Tissues, Feature selection, Computer aided diagnosis and therapy, Image segmentation, Skin, Nipple, Feature extraction, Diagnostics, Databases

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Proceedings Article | 3 March 2009 Paper

Using volumetric density estimation in computer aided mass detection in mammography

Michiel Kallenberg, Nico Karssemeijer

Proceedings Volume 7260, 72600T (2009) https://doi.org/10.1117/12.810243

KEYWORDS: Tissues, CAD systems, Breast, Image segmentation, Computer aided design, Mammography, Optical spheres, Signal attenuation, Computer aided diagnosis and therapy, Spherical lenses

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Proceedings Article | 17 March 2008 Paper

The effect of training with SFM images in a FFDM CAD system

Michiel Kallenberg, Nico Karssemeijer

Proceedings Volume 6915, 69151O (2008) https://doi.org/10.1117/12.767681

KEYWORDS: Atomic force microscopy, Mammography, CAD systems, Absorbance, Databases, Digital mammography, Image segmentation, Computer aided design, Scanning electron microscopy, Automatic exposure

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Proceedings Article | 17 March 2008 Paper

Feature selection for computer-aided detection: comparing different selection criteria

Rianne Hupse, Nico Karssemeijer

Proceedings Volume 6915, 691503 (2008) https://doi.org/10.1117/12.771972

KEYWORDS: Feature selection, Feature extraction, Computer aided diagnosis and therapy, Neural networks, Image segmentation, Databases, Mammography, Cancer, Statistical analysis

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Proceedings Article | 17 March 2008 Paper

Matching mammographic regions in mediolateral oblique and cranio caudal views: a probabilistic approach

Maurice Samulski, Nico Karssemeijer

Proceedings Volume 6915, 69151M (2008) https://doi.org/10.1117/12.769792

KEYWORDS: Mammography, Image segmentation, Breast, Nipple, CAD systems, Computer aided diagnosis and therapy, Analytical research, Statistical analysis, Computer aided design, Tissues

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Proceedings Article | 30 March 2007 Paper

Classification of mammographic masses using support vector machines and Bayesian networks

Maurice Samulski, Nico Karssemeijer, Peter Lucas, Perry Groot

Proceedings Volume 6514, 65141J (2007) https://doi.org/10.1117/12.709679

KEYWORDS: Image segmentation, Principal component analysis, Statistical analysis, Breast, Mammography, Tissues, Image classification, Computer aided diagnosis and therapy, Feature extraction, Medical imaging

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Proceedings Article | 29 April 2005 Paper

Thickness correction of mammographic images by anisotropic filtering and interpolation of dense tissue

Peter Snoeren, Nico Karssemeijer

Proceedings Volume 5747, (2005) https://doi.org/10.1117/12.595255

KEYWORDS: Breast, Tissues, Anisotropic filtering, Image filtering, Image processing, Anisotropic diffusion, Medical imaging, Displays, Image compression, Mammography

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Proceedings Article | 29 April 2005 Paper

Regrouping initial CAD mass detections to facilitate classification of suspicious regions in mammography

Saskia van Engeland, Nico Karssemeijer

Proceedings Volume 5747, (2005) https://doi.org/10.1117/12.595205

KEYWORDS: Detection and tracking algorithms, Mammography, Computer aided diagnosis and therapy, Image segmentation, Computer aided design, Breast, Algorithm development, Computer programming, Image processing algorithms and systems, Image classification

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Proceedings Article | 6 April 2005 Paper

Using context for mass detection and classification in mammograms

Saskia van Engeland, Celia Varela, Sheila Timp, Peter Snoeren, Nico Karssemeijer

Proceedings Volume 5749, (2005) https://doi.org/10.1117/12.594483

KEYWORDS: Mammography, Computer aided design, Computer aided diagnosis and therapy, Breast, Medical imaging, Image classification, Feature extraction, Statistical analysis, Radiology, Breast cancer

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Proceedings Article | 4 May 2004 Paper

Effect of independent multiple reading of mammograms on detection performance

Nico Karssemeijer, Johannes Otten, Antonius A. Roelofs, Sander van Woudenberg, Jan Hendriks

Proceedings Volume 5372, (2004) https://doi.org/10.1117/12.535225

KEYWORDS: Mammography, Cancer, Breast cancer, Computer aided design, Visualization, Medical imaging, Image resolution, Architectural distortion, Image enhancement, Digital filtering

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Proceedings Article | 22 May 2003 Paper

Optimized soft-copy display of digitized mammograms

Ton Roelofs, Sander van Woudenberg, Jan Hendriks, Nico Karssemeijer

Proceedings Volume 5034, (2003) https://doi.org/10.1117/12.479981

KEYWORDS: Mammography, Breast, Image quality, CRTs, Image enhancement, Image quality standards, Breast cancer, Digital mammography, Computer aided diagnosis and therapy, Image retrieval

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Proceedings Article | 22 May 2003 Paper

Optimized perception of lesion growth in mammograms using digital display

Saskia van Engeland, Peter Snoeren, Nico Karssemeijer, Jan Hendriks

Proceedings Volume 5034, (2003) https://doi.org/10.1117/12.480074

KEYWORDS: Mammography, Cancer, Breast, Digital mammography, Radiology, Diagnostics, Medical imaging, Nipple, Opacity, Curium

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Proceedings Article | 15 May 2003 Paper

Mammographic mass characterization using sharpness and lobulation measures

Celia Varela, J. Muller, Nico Karssemeijer

Proceedings Volume 5032, (2003) https://doi.org/10.1117/12.480161

KEYWORDS: Algorithm development, Mammography, Image segmentation, Databases, Cancer, Breast, Feature extraction, Digital mammography, Feature selection, Tissues

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Proceedings Article | 21 May 1999 Paper

Improved method for detection of microcalcification clusters in digital mammograms

Wouter Veldkamp, Nico Karssemeijer

Proceedings Volume 3661, (1999) https://doi.org/10.1117/12.348607

KEYWORDS: Mammography, Feature selection, Image segmentation, Feature extraction, Breast, Mahalanobis distance, Image processing, Distance measurement, Statistical methods, Databases

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Proceedings Article | 21 May 1999 Paper

Discrete dynamic contour model for mass segmentation in digital mammograms

Guido te Brake, Mark Stoutjesdijk, Nico Karssemeijer

Proceedings Volume 3661, (1999) https://doi.org/10.1117/12.348651

KEYWORDS: Image segmentation, Mammography, Tumors, Algorithm development, Medical imaging, Image processing algorithms and systems, Computing systems, Signal detection, Tissues, Image analysis

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Proceedings Article | 29 July 1993 Paper

Recognition of clustered microcalcifications using a random field model

Nico Karssemeijer

Proceedings Volume 1905, (1993) https://doi.org/10.1117/12.148689

KEYWORDS: Mammography, Image processing, Image segmentation, Tissues, Algorithm development, Image analysis, Sensors, Image filtering, CCD cameras, Detection and tracking algorithms

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Proceedings Article | 29 July 1993 Paper

Common database for research in mammographic image analysis

Nico Karssemeijer

Proceedings Volume 1905, (1993) https://doi.org/10.1117/12.148617

KEYWORDS: Databases, Image analysis, Mammography, Image quality, Diagnostics, Spatial resolution, Calibration, Algorithm development, Radiology, Analytical research

Proceedings Article | 1 June 1991 Paper

Iso-precision scaling of digitized mammograms to facilitate image analysis

Nico Karssemeijer, Leon van Erning

Proceedings Volume 1445, (1991) https://doi.org/10.1117/12.45214

KEYWORDS: Sensors, Quantization, Image processing, Interference (communication), Mammography, CCD image sensors, Image analysis, Signal detection, Absorbance, Photodiodes

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Showing 5 of 66 publications

Proceedings Volume Editor (4)

SPIE Conference Volume | 9 March 2010

Medical Imaging 2010: Computer-Aided Diagnosis

SPIE Conference Volume | 27 February 2009

Medical Imaging 2009: Computer-Aided Diagnosis

SPIE Conference Volume | 11 April 2008

Medical Imaging 2008: Computer-Aided Diagnosis

SPIE Conference Volume | 8 March 2007

Medical Imaging 2007: Computer-Aided Diagnosis

Conference Committee Involvement (17)

Computer-Aided Diagnosis

21 February 2022 | San Diego, California, United States

Computer-Aided Diagnosis

15 February 2021 | Online Only, California, United States

Computer-Aided Diagnosis

16 February 2020 | Houston, Texas, United States

Computer-Aided Diagnosis

17 February 2019 | San Diego, California, United States

Computer-Aided Diagnosis

12 February 2018 | Houston, Texas, United States

Computer-Aided Diagnosis

13 February 2017 | Orlando, Florida, United States

Computer-Aided Diagnosis Posters

13 February 2017 | Orlando, FL, United States

Computer-Aided Diagnosis

28 February 2016 | San Diego, California, United States

Computer-Aided Diagnosis

22 February 2015 | Orlando, Florida, United States

Computer-Aided Diagnosis

18 February 2014 | San Diego, California, United States

SPIE Medical Imaging

9 February 2013 | Lake Buena Vista (Orlando Area), United States

SPIE Medical Imaging

4 February 2012 | San Diego, United States

Computer-Aided Diagnosis

15 February 2011 | Lake Buena Vista (Orlando), Florida, United States

Computer-Aided Diagnosis

16 February 2010 | San Diego, California, United States

Computer-Aided Diagnosis

10 February 2009 | Lake Buena Vista (Orlando Area), Florida, United States

Computer-Aided Diagnosis

19 February 2008 | San Diego, California, United States

Computer-Aided Diagnosis

20 February 2007 | San Diego, CA, United States

Showing 5 of 17 Conference Committees

Course Instructor

SC882: Computer-Aided Diagnosis

The interpretation of medical images is expected to benefit from computer technology with advances in CAD. Computer-aided diagnosis (CAD) can be defined as a diagnosis made by a radiologist who uses the output from a computerized analysis of medical images as a second opinion in detecting lesions and in making diagnostic decisions. The final diagnosis is made by the radiologist. Rationale, computerized image analysis methods, evaluation methods, and translational clinical studies of CAD in the detection, diagnosis, and risk assessment of cancer will be reviewed. Specific examples will be presented in breast imaging, thoracic imaging, and colonography.

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