Publications of Nico Karssemeijer

2016

Papers in international journals

1. M. Ghafoorian, N. Karssemeijer, I. van Uden, F. de Leeuw, T. Heskes, E. Marchiori and B. Platel, "Automated Detection of White Matter Hyperintensities of All Sizes in Cerebral Small Vessel Disease", Medical Physics, 2016;43(12):6246-6258.
   Abstract DOI PMID Cited by ~61
2. K. Holland, J. van Zelst, G. den Heeten, M. Imhof-Tas, R. Mann, C. van Gils and N. Karssemeijer, "Consistency of breast density categories in serial screening mammograms: A comparison between automated and human assessment", Breast, 2016;29:49-54.
   Abstract DOI PMID Cited by ~20
3. T. Tan, A. Gubern-Mérida, C. Borelli, R. Manniesing, J. van Zelst, L. Wang, W. Zhang, B. Platel, R. Mann and N. Karssemeijer, "Segmentation of malignant lesions in 3D breast ultrasound using a depth-dependent model", Medical Physics, 2016;43(7):4074-4084.
   Abstract DOI PMID Cited by ~15
4. B. Bejnordi, M. Balkenhol, G. Litjens, R. Holland, P. Bult, N. Karssemeijer and J. van der Laak, "Automated Detection of DCIS in Whole-Slide H&E Stained Breast Histopathology Images", IEEE Transactions on Medical Imaging, 2016;35(9):2141-2150.
   Abstract DOI PMID Cited by ~83
5. J. Mordang, A. Gubern-Mérida, G. den Heeten and N. Karssemeijer, "Reducing false positives of microcalcification detection systems by removal of breast arterial calcifications", Medical Physics, 2016;43(4):1676-1687.
   Abstract DOI PMID Cited by ~13
6. M. Kallenberg, K. Petersen, M. Nielsen, A. Ng, P. Diao, C. Igel, C. Vachon, K. Holland, N. Karssemeijer and M. Lillholm, "Unsupervised deep learning applied to breast density segmentation and mammographic risk scoring", IEEE Transactions on Medical Imaging, 2016;35:1322-1331.
   Abstract DOI PMID Cited by ~356
7. S. Schalekamp, N. Karssemeijer, A. Cats, B. De Hoop, B. Geurts, O. Berger-Hartog, B. van Ginneken and C. Schaefer-Prokop, "The Effect of Supplementary Bone-Suppressed Chest Radiographs on the Assessment of a Variety of Common Pulmonary Abnormalities: Results of an Observer Study", Journal of Thoracic Imaging, 2016;31(2):119-125.
   Abstract DOI PMID Download Cited by ~7
8. A. Gubern-Mérida, S. Vreemann, R. Marti, J. Melendez, S. Lardenoije, R. Mann, N. Karssemeijer and B. Platel, "Automated detection of breast cancer in false-negative screening MRI studies from women at increased risk", European Journal of Radiology, 2016;85(2):472-479.
   Abstract DOI PMID Cited by ~25
9. M. Dalmis, A. Gubern-Mérida, S. Vreemann, N. Karssemeijer, R. Mann and B. Platel, "A Computer-Aided Diagnosis System for Breast DCE-MRI at High Spatiotemporal Resolution", Medical Physics, 2016;43(1):84-94.
   Abstract DOI PMID Cited by ~31
10. B. Bejnordi, G. Litjens, N. Timofeeva, I. Otte-Holler, A. Homeyer, N. Karssemeijer and J. van der Laak, "Stain specific standardization of whole-slide histopathological images", IEEE Transactions on Medical Imaging, 2016;35(2):404-415.
    Abstract DOI PMID Cited by ~250

Papers in conference proceedings

1. M. Ghafoorian, N. Karssemeijer, T. Heskes, I. van Uden, F. de Leeuw, E. Marchiori, B. van Ginneken and B. Platel, "Non-uniform patch sampling with deep convolutional neural networks for white matter hyperintensity segmentation", IEEE International Symposium on Biomedical Imaging, 2016:1414-1417.
   Abstract DOI Cited by ~72
2. A. Bria, C. Marrocco, J. Mordang, N. Karssemeijer, M. Molinara and F. Tortorella, "LUT-QNE: Look-Up-Table Quantum Noise Equalization in Digital Mammograms", Breast Imaging, 2016;9699:27-34.
   Abstract DOI Cited by ~5
3. K. Holland, I. Sechopoulos, G. den Heeten, R. Mann and N. Karssemeijer, "Performance of breast cancer screening depends on mammographic compression", Breast Imaging, 2016;9699:183-189.
   Abstract DOI Cited by ~17
4. M. Kallenberg, M. Nielsen, K. Holland, N. Karssemeijer, C. Igel and M. Lillholm, "Learning Density Independent Texture Features", Breast Imaging, 2016;9699:299-306.
   Abstract DOI
5. M. Ufuk Dalmiş, A. Gubern-Mérida, C. Borelli, S. Vreemann, R. Mann and N. Karssemeijer, "A fully automated system for quantification of background parenchymal enhancement in breast DCE-MRI", Medical Imaging 2016: Computer-Aided Diagnosis, 2016.
   Abstract DOI Cited by ~6
6. J. Mordang, T. Janssen, A. Bria, T. Kooi, A. Gubern-Mérida and N. Karssemeijer, "Automatic Microcalcification Detection in Multi-vendor Mammography Using Convolutional Neural Networks", Breast Imaging, 2016;9699:35-42.
   Abstract DOI Cited by ~72
7. T. Kooi, A. Gubern-Mérida, J. Mordang, R. Mann, R. Pijnappel, K. Schuur, A. den Heeten and N. Karssemeijer, "A Comparison Between a Deep Convolutional Neural Network and Radiologists for Classifying Regions of Interest in Mammography", Breast Imaging, 2016;9699:51-56.
   Abstract DOI Cited by ~46
8. A. Gubern-Mérida, T. Tan, J. van Zelst, R. Mann and N. Karssemeijer, "Automated linking of suspicious findings between automated 3D breast ultrasound volumes", Medical Imaging, 2016.
   Abstract DOI Cited by ~2
9. T. Mertzanidou, J. Hipwell, S. Reis, B. Bejnordi, M. Hermsen, M. Dalmis, S. Vreemann, B. Platel, J. van der Laak, N. Karssemeijer, R. Mann, P. Bult and D. Hawkes, "Whole Mastectomy Volume Reconstruction from 2D Radiographs and Its Mapping to Histology", Breast Imaging, 2016;9699:367-374.
   Abstract DOI Cited by ~4
10. A. Bria, C. Marrocco, N. Karssemeijer, M. Molinara and F. Tortorella, "Deep Cascade Classifiers to Detect Clusters of Microcalcifications", Breast Imaging, 2016;9699:415-422.
    Abstract DOI Cited by ~16
11. K. Holland, C. van Gils, J. Wanders, R. Mann and N. Karssemeijer, "Quantification of mammographic masking risk with volumetric breast density maps: How to select women for supplemental screening", Medical Imaging, 2016.
    Abstract DOI Cited by ~4

Abstracts

1. S. Vreemann, A. Gubern-Mérida, C. Borelli, N. Karssemeijer and R. Mann, "Background Parenchymal Enhancement as a predictor of breast cancer grade: a pilot study", European Congress of Radiology, 2016.
   Abstract
2. S. Vreemann, A. Gubern-Mérida, S. Lardenoije, N. Karssemeijer and R. Mann, "Differences between cancers detected in prophylactic mastectomy specimen, screen detected cancers and true interval cancers in women participating in an intermediate and high risk screening program", European Breast Cancer Conference, 2016.
   Abstract
3. N. Karssemeijer, K. Holland, I. Sechopoulos, R. Mann, G. den Heeten and C. van Gils, "High Breast Compression in Mammography May Reduce Sensitivity", Annual Meeting of the Radiological Society of North America, 2016.
   Abstract
4. K. Kallenberg, M. Nielsen, K. Holland, N. Karssemeijer and M. Lillholm, "Breast Cancer Risk Prediction with Density Independent Texture Features", Annual Meeting of the Radiological Society of North America, 2016.
   Abstract
5. S. Vreemann, A. Gubern-Mérida, S. Lardenoije, N. Karssemeijer and R. Mann, "Prognostic factors of interval carcinomas occurring in an intermediate and high risk breast cancer screening program", European Congress of Radiology, 2016.
   Abstract
6. J. Wanders, K. Holland, P. Peeters, N. Karssemeijer and C. van Gils, "Volumetric Breast Density And The Risk Of Screen-Detected And Interval Breast Cancer", Annual conference of the International Agency for Research on Cancer, 2016.
   Abstract
7. S. Vreemann, A. Gubern-Mérida, S. Lardenoije, N. Karssemeijer and R. Mann, "The performance of MRI screening in the detection of breast cancer in an intermediate and high risk screening program", Annual Meeting of the International Society for Magnetic Resonance in Medicine, 2016.
   Abstract

PhD theses

1. W. van de Ven, "MRI guided TRUS prostate biopsy - a viable alternative?", PhD thesis, 2016.
   Abstract Url

Other publications

1. M. Razavi, L. Wang, T. Tan, N. Karssemeijer, L. Linsen, U. Frese, H. Hahn and G. Zachmann, "Novel Morphological Features for Non-mass-like Breast Lesion Classification on DCE-MRI", Machine Learning in Medical Imaging, 2016:305-312.
   Abstract DOI Cited by ~2