U. Mahmood, A. Shukla-Dave, H. Chan, K. Drukker, R. Samala, Q. Chen, D. Vergara, H. Greenspan, N. Petrick, B. Sahiner, Z. Huo, R. Summers, K. Cha, G. Tourassi, T. Deserno, K. Grizzard, J. Näppi, H. Yoshida, D. Regge, R. Mazurchuk, K. Suzuki, L. Morra, H. Huisman, S. Armato and L. Hadjiiski, "Artificial intelligence in medicine: mitigating risks and maximizing benefits via quality assurance, quality control, and acceptance testing",
BJR|Artificial Intelligence,
2024;1.
T. Perik, N. Alves, J. Hermans and H. Huisman, "Automated Quantitative Analysis of CT Perfusion to Classify Vascular Phenotypes of Pancreatic Ductal Adenocarcinoma",
Cancer,
2024;16(3):577.
B. Russell, K. Beyer, A. Lawlor, M. Roobol, L. Venderbos, S. Remmers, E. Briers, S. MacLennan, S. MacLennan, M. Omar, M. Van Hemelrijck, E. Smith, J. N'Dow, K. Plass, M. Ribal, N. Mottet, R. Shepherd, T. Abbott, K. Mastris, L. Moris, M. Lardas, T. den Van Broeck, P. Willemse, N. Fossati, K. Pang, R. Campi, I. Greco, M. Gacci, S. Serni, A. Bjartell, R. Lonnerbro, A. Briganti, D. Crosti, R. Garzonio, G. Gandaglia, M. Faticoni, G. office, C. Bangma, M. Jongerden, D. Tilki, A. Auvinen, T. Murtola, T. Visakorpi, K. Talala, T. Tammela, A. Siltari, S. Lejeune, L. Colette, S. Caputova, D. Poli, S. Byrne, L. Fialho, A. Rowland, N. Tapela, N. Di Flora, K. Apostolidis, V. Lemair, B. De Meulder, C. Auffray, N. Taibi, A. Hijazy, A. Saporta, K. Sun, S. Power, N. Zounemat Kermani, K. van Bochove, A. Tafreshiha, C. Bernini, D. Horgan, L. Fullwood, M. Holtorf, D. Lancet, G. Bernstein, S. Tripathee, M. Wirth, M. Froehner, B. Brenner, A. Borkowetz, C. Thomas, F. Horn, K. Reiche, M. Kreuz, A. Josefsson, D. Gasi Tandefelt, J. Hugosson, J. Schalken, H. Huisman, T. Hofmarcher, P. Lindgren, E. Andersson, A. Fridhammar, M. Tames Grijalva, S. Evans-Axelsson, F. Verholen, J. Zong, J. Butler-Ransohoff, T. Williamson, R. Waldeck, A. Bruno, E. Nevedomskaya, S. Fatoba, N. Constantinovici, C. Steinbeisser, M. Maass, P. Torremante, E. Dochy, F. Pisa, M. Voss, K. Papineni, J. Wang-silvanto, R. Snijder, X. Wang, M. Lambrecht, R. Wolfinger, S. Eid, S. Palanisamy, S. Haque, L. Antoni, A. Servan, K. Pascoe, P. Robinson, J. Lencart, B. Jaton, H. Turunen, O. Kilkku, P. Pohjanjousi, O. Voima, L. Nevalaita, K. Punakivi, S. Seager, S. Ratwani, K. Grzeslak, J. Brash, E. Longden-Chapman, D. Burke, M. Licour, S. Payne, A. Yong, F. Lujan, S. Le Mare, J. Hendrich, M. Bussmann, Juckeland, Kotik, D. Poli and C. Reich, "Survivorship Data in Prostate Cancer: Where Are We and Where Do We Need To Be?",
European Urology Open Science,
2024;59:27-29.
C. Noordman, D. Yakar, J. Bosma, F. Simonis and H. Huisman, "Complexities of deep learning-based undersampled MR image reconstruction",
European Radiology Experimental,
2023;7.
N. Alves, J. Bosma, K. Venkadesh, C. Jacobs, Z. Saghir, M. de Rooij, J. Hermans and H. Huisman, "Prediction Variability to Identify Reduced AI Performance in Cancer Diagnosis at MRI and CT",
Radiology,
2023;308.
Y. Li, Y. Fu, I. Gayo, Q. Yang, Z. Min, S. Saeed, W. Yan, Y. Wang, J. Noble, M. Emberton, M. Clarkson, H. Huisman, D. Barratt, V. Prisacariu and Y. Hu, "Prototypical few-shot segmentation for cross-institution male pelvic structures with spatial registration",
Medical Image Analysis,
2023;90:102935.
J. Bleker, C. Roest, D. Yakar, H. Huisman and T. Kwee, "The Effect of Image Resampling on the Performance of Radiomics-Based Artificial Intelligence in Multicenter Prostate MRI",
Journal of Magnetic Resonance Imaging,
2023.
M. Schuurmans, N. Alves, P. Vendittelli, H. Huisman and J. Hermans, "Artificial Intelligence in Pancreatic Ductal Adenocarcinoma Imaging: A Commentary on Potential Future Applications.",
Gastroenterology,
2023.
M. Omar, S. MacLennan, M. Ribal, M. Roobol, K. Dimitropoulos, T. van den Broeck, S. MacLennan, S. Axelsson, G. Gandaglia, P. Willemse, K. Mastris, J. Ransohoff, Z. Devecseri, T. Abbott, B. De Meulder, A. Bjartell, A. Asiimwe, J. N'Dow, E. Smith, K. Plass, N. Mottet, R. Shepherd, L. Moris, M. Lardas, N. Fossati, K. Pang, R. Campi, I. Greco, M. Gacci, S. Serni, R. Lonnerbro, A. Briganti, D. Crosti, R. Garzonio, M. Faticoni, C. Bangma, E. Roest, A. Breederland, S. Remmers, D. Tilki, A. Auvinen, T. Murtola, T. Visakorpi, K. Talala, T. Tammela, A. Siltari, M. Van Hemelrijck, K. Beyer, S. Lejeune, L. Colette, S. Caputova, D. Poli, S. van Dorp, S. Byrne, L. Fialho, A. Rowland, N. Tapela, F. Ugolini, C. Auffray, N. Taibi, A. Hijazy, A. Saporta, K. Sun, S. Power, N. Kermani, K. van Bochove, M. Moinat, M. Kalafati, A. Tafreshiha, C. Bernini, K. Hlavati, D. Horgan, L. Fullwood, M. Holtorf, D. Lancet, G. Bernstein, S. Tripathee, M. Wirth, M. Froehner, B. Brenner, A. Borkowetz, C. Thomas, F. Horn, K. Reiche, M. Kreuz, A. Josefsson, D. Tandefelt, J. Hugosson, J. Schalken, H. Huisman, T. Hofmarcher, P. Lindgren, E. Andersson, A. Fridhammar, M. Grijalva, F. Verholen, J. Zong, T. Williamson, K. Chandrawansa, R. Waldeck, A. Bruno, R. Herrera, E. Nevedomskaya, S. Fatoba, N. Constantinovici, A. Mohamed, C. Steinbeisser, M. Maass, P. Torremante, E. Dochy, F. Pisa, M. Voss, A. Kiran, K. Papineni, J. Wang-silvanto, R. Snijder, X. Wang, M. Lambrecht, R. Wolfinger, L. Antoni, A. Servan, K. Pascoe, P. Robinson, B. Jaton, D. Bakkard, H. Turunen, O. Kilkku, P. Pohjanjousi, O. Voima, L. Nevalaita, K. Punakivi, C. Reich, S. Seager, S. Ratwani, E. Longden-Chapman, D. Burke, M. Licour, S. Payne, A. Yong, F. Lujan, S. Le Mare, J. Hendrich, M. Bussmann, G. Juckeland, D. Kotik and T. Consortium, "Unanswered questions in prostate cancer -- findings of an international multi-stakeholder consensus by the PIONEER consortium",
Nature Reviews Urology,
2023;20:494-501.
L. Hu, C. Fu, X. Song, R. Grimm, H. von Busch, T. Benkert, A. Kamen, B. Lou, H. Huisman, A. Tong, T. Penzkofer, M. Choi, I. Shabunin, D. Winkel, P. Xing, D. Szolar, F. Coakley, S. Shea, E. Szurowska, J. Guo, L. Li, Y. Li and J. Zhao, "Automated deep-learning system in the assessment of MRI-visible prostate cancer: comparison of advanced zoomed diffusion-weighted imaging and conventional technique.",
Cancer imaging : the official publication of the International Cancer Imaging Society,
2023;23(1):6.
J. Bosma, A. Saha, M. Hosseinzadeh, I. Slootweg, M. de Rooij and H. Huisman, "Semi-supervised Learning with Report-guided Pseudo Labels for Deep Learning-based Prostate Cancer Detection Using Biparametric MRI",
Radiology: Artificial Intelligence,
2023:e230031.
B. van den Beukel, B. de Wilde, F. Joosten, H. van Goor, W. Venderink, H. Huisman and R. ten Broek, "Quantifiable Measures of Abdominal Wall Motion for Quality Assessment of Cine-MRI Slices in Detection of Abdominal Adhesions",
Journal of Imaging,
2023;9(5).
L. Thijssen, M. de Rooij, J. Barentsz and H. Huisman, "Radiomics based automated quality assessment for T2W prostate MR images",
European Journal of Radiology,
2023.
B. de Wilde, F. Joosten, W. Venderink, M. Davidse, J. Geurts, H. Kruijt, A. Vermeulen, B. Martens, M. Schyns, J. Huige, M. de Boer, B. Tonino, H. Zandvoort, K. Lammert, H. Parviainen, A. Vuorinen, S. Syvaranta, R. Vogels, W. Prins, A. Coppola, N. Bossa, R. ten Broek and H. Huisman, "Inter-and Intra-Observer Variability and the Effect of Experience in Cine-MRI for Adhesion Detection",
Journal of Imaging,
2023;9(3):55.
B. de Wilde, A. Saha, R. ten Broek and H. Huisman, "Medical diffusion on a budget: textual inversion for medical image generation",
arXiv:2303.13430,
2023.
M. Hosseinzadeh, A. Saha, J. Bosma and H. Huisman, "Uncertainty-Aware Semi-Supervised Learning for Prostate MRI Zonal Segmentation",
arXiv:2305.05984,
2023.
J. Bosma, D. Peeters, N. Alves, A. Saha, Z. Saghir, C. Jacobs and H. Huisman, "Reproducibility of Training Deep Learning Models for Medical Image Analysis",
Medical Imaging with Deep Learning,
2023.
A. Saha, J. Bosma, J. Twilt, B. van Ginneken, D. Yakar, M. Elschot, J. Veltman, J. Futterer, M. de Rooij and H. Huisman, "Artificial Intelligence and Radiologists at Prostate Cancer Detection in MRI: The PI-CAI Challenge",
Medical Imaging with Deep Learning,
2023.
Q. van Lohuizen, C. Roest, F. Simonis, S. Fransen, T. Kwee, D. Yakar and H. Huisman, "Diagnostic AI to speed up MRI protocols by identifying redundant sequences: are all diffusion-weighted prostate MRI sequences necessary?",
Annual Meeting of the Radiological Society of North America,
2023.
J. Twilt, A. Saha, J. Bosma, B. van Ginneken, D. Yakar, M. Elschot, J. Veltman, J. Futterer, H. Huisman and M. de Rooij, "EAU Plenary Gamechanging Session - Artificial Intelligence and Radiologists at Prostate Cancer Detection in MRI: Preliminary Results from the PI-CAI Challenge",
Annual European Association of Urology Congress,
2023.
J. Twilt, A. Saha, J. Bosma, B. van Ginneken, D. Yakar, M. Elschot, J. Veltman, J. Futterer, H. Huisman and M. de Rooij, "International Comparative Study of Artificial Intelligence and Radiologists in Clinically Significant Prostate Cancer Detection: Results From The PI-CAI Consortium",
Annual Meeting of the Society for Advanced Body Imaging,
2023.
J. Twilt, A. Saha, J. Bosma, B. van Ginneken, D. Yakar, M. Elschot, J. Veltman, J. Futterer, H. Huisman and M. de Rooij, "Diagnostic Value of Dynamic Contrast-Enhanced MRI for the Detection of Clinically Significant Prostate Cancer in a Multi-Reader Study: Preliminary Results from the PI-CAI Consortium",
European Congress of Radiology,
2023.
D. Peeters, N. Alves, K. Venkadesh, R. Dinnessen, Z. Saghir, E. Scholten, H. Huisman, C. Schaefer-Prokop, R. Vliegenthart, M. Prokop and C. Jacobs, "The effect of applying an uncertainty estimation method on the performance of a deep learning model for nodule malignancy risk estimation",
European Congress of Radiology,
2023.
A. Saha, J. Bosma, J. Twilt, B. van Ginneken, D. Yakar, M. Elschot, J. Veltman, J. Futterer, M. de Rooij and H. Huisman, "Artificial Intelligence and Radiologists at Prostate Cancer Detection in MRI: The PI-CAI Challenge",
European Congress of Radiology,
2023.
M. Antonelli, A. Reinke, S. Bakas, K. Farahani, A. Kopp-Schneider, B. Landman, G. Litjens, B. Menze, O. Ronneberger, R. Summers, B. van Ginneken, M. Bilello, P. Bilic, P. Christ, R. Do, M. Gollub, S. Heckers, H. Huisman, W. Jarnagin, M. McHugo, S. Napel, J. Pernicka, K. Rhode, C. Tobon-Gomez, E. Vorontsov, J. Meakin, S. Ourselin, M. Wiesenfarth, P. Arbelaez, B. Bae, S. Chen, L. Daza, J. Feng, B. He, F. Isensee, Y. Ji, F. Jia, I. Kim, K. Maier-Hein, D. Merhof, A. Pai, B. Park, M. Perslev, R. Rezaiifar, O. Rippel, I. Sarasua, W. Shen, J. Son, C. Wachinger, L. Wang, Y. Wang, Y. Xia, D. Xu, Z. Xu, Y. Zheng, A. Simpson, L. Maier-Hein and M. Cardoso, "The Medical Segmentation Decathlon",
Nature Communications,
2022;13(1):4128.
N. Alves, M. Schuurmans, G. Litjens, J. Bosma, J. Hermans and H. Huisman, "Fully Automatic Deep Learning Framework for Pancreatic Ductal Adenocarcinoma Detection on Computed Tomography",
Cancers,
2022:376.
M. Sunoqrot, A. Saha, M. Hosseinzadeh, M. Elschot and H. Huisman, "Artificial Intelligence for Prostate MRI: Open Datasets, Available Applications, and Grand Challenges",
European Radiology Experimental,
2022:35.
M. Schuurmans, N. Alves, P. Vendittelli, H. Huisman and J. Hermans, "Setting the Research Agenda for Clinical Artificial Intelligence in Pancreatic Adenocarcinoma Imaging",
Cancers,
2022:3498.
C. Roest, T. Kwee, A. Saha, J. Futterer, D. Yakar and H. Huisman, "AI-Assisted Biparametric MRI Surveillance of Prostate Cancer: Feasibility Study",
European Radiology,
2022.
J. Bleker, T. Kwee, D. Rouw, C. Roest, J. Borstlap, I. de Jong, R. Dierckx, H. Huisman and D. Yakar, "A deep learning masked segmentation alternative to manual segmentation in biparametric MRI prostate cancer radiomics",
European Radiology,
2022:1-10.
S. Labus, M. Altmann, H. Huisman, A. Tong, T. Penzkofer, M. Choi, I. Shabunin, D. Winkel, P. Xinga, D. Szolar, S. Shea, R. Grimm, H. von Busch, A. Kamen, T. Herold and C. Baumann, "A concurrent, deep learning-based computer-aided detection system for prostate multiparametric MRI: a performance study involving experienced and less-experienced radiologists",
European Radiology,
2022.
L. Hadjiiski, K. Cha, H. Chan, K. Drukker, L. Morra, J. Nappi, B. Sahiner, H. Yoshida, Q. Chen, T. Deserno, H. Greenspan, H. Huisman, Z. Huo, R. Mazurchuk, N. Petrick, D. Regge, R. Samala, R. Summers, K. Suzuki, G. Tourassi, D. Vergara and S. III, "AAPM task group report 273: Recommendations on best practices for AI and machine learning for computer-aided diagnosis in medical imaging",
Medical Physics,
2022.
Y. Li, Y. Fu, Q. Yang, Z. Min, W. Yan, H. Huisman, D. Barratt, V. Prisacariu and Y. Hu, "FEW-SHOT Image Segmentation for Cross-Institution Male Pelvic Organs Using Registration-Assisted Prototypical Learning",
2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI),
2022.
M. Schuurmans, N. Alves, H. Huisman and J. Hermans, "Deep Learning for Detection of Iso-attenuating Pancreatic Adenocarcinoma in Computed Tomography",
Annual Meeting of the Radiological Society of North America,
2022.
S. de Jong, N. Alves, M. Schuurmans, J. Hermans and H. Huisman, "Deep Learning for Automatic Contrast Enhancement Phase Detection on Abdominal Computed Tomography",
Annual Meeting of the Radiological Society of North America,
2022.
C. Roest, T. Kwee, A. Saha, J. Futterer, D. Yakar and H. Huisman, "AI-Assisted Biparametric MRI Surveillance of Prostate Cancer: Feasibility Study",
European Congress of Radiology,
2022.
T. Perik, J. Hermans and H. Huisman, "AI-assisted analysis of CT perfusion to predict response in patients with pancreatic adenocarcinoma",
European Congress of Radiology,
2022.
N. Alves, J. Bosma and H. Huisman, "Towards Safe Clinical Use of Artificial Intelligence for Cancer Detection Through Uncertainty Quantification",
Annual Meeting of the Radiological Society of North America,
2022.
S. Fransen, C. Roest, Q. van Lohuizen, J. Bosma, T. Kwee, D. Yakar and H. Huisman, "Diagnostic AI to speed up MRI protocols by identifying redundant sequences: are all diffusion-weighted prostate MRI sequences necessary?",
Annual Meeting of the Radiological Society of North America,
2022.
A. Saha, J. Bosma, J. Twilt, B. van Ginneken, D. Yakar, M. Elschot, J. Veltman, J. Futterer, M. de Rooij and H. Huisman, "Artificial Intelligence and Radiologists at Prostate Cancer Detection in MRI: The PI-CAI Challenge",
Annual Meeting of the Radiological Society of North America,
2022.
S. Youn, M. Choi, D. Kim, Y. Lee, H. Huisman, E. Johnson, T. Penzkofer, I. Shabunin, D. Winkel, P. Xing, D. Szolar, R. Grimm, H. von Busch, Y. Son, B. Lou and A. Kamen, "Detection and PI-RADS classification of focal lesions in prostate MRI: Performance comparison between a deep learning-based algorithm (DLA) and radiologists with various levels of experience",
European Journal of Radiology,
2021;142:109894.
J. Twilt, K. van Leeuwen, H. Huisman, J. Fütterer and M. de Rooij, "Artificial Intelligence Based Algorithms for Prostate Cancer Classification and Detection on Magnetic Resonance Imaging: A Narrative Review",
Diagnostics,
2021;11:959.
T. Penzkofer, A. Padhani, B. Turkbey, M. Haider, H. Huisman, J. Walz, G. Salomon, I. Schoots, J. Richenberg, G. Villeirs, V. Panebianco, O. Rouviere, V. Logager and J. Barentsz, "ESUR/ESUI position paper: developing artificial intelligence for precision diagnosis of prostate cancer using magnetic resonance imaging.",
European Radiology,
2021.
N. Lessmann, C. Sánchez, L. Beenen, L. Boulogne, M. Brink, E. Calli, J. Charbonnier, T. Dofferhoff, W. van Everdingen, P. Gerke, B. Geurts, H. Gietema, M. Groeneveld, L. van Harten, N. Hendrix, W. Hendrix, H. Huisman, I. Isgum, C. Jacobs, R. Kluge, M. Kok, J. Krdzalic, B. Lassen-Schmidt, K. van Leeuwen, J. Meakin, M. Overkamp, T. van Rees Vellinga, E. van Rikxoort, R. Samperna, C. Schaefer-Prokop, S. Schalekamp, E. Scholten, C. Sital, L. Stöger, J. Teuwen, K. Vaidhya Venkadesh, C. de Vente, M. Vermaat, W. Xie, B. de Wilde, M. Prokop and B. van Ginneken, "Automated Assessment of COVID-19 Reporting and Data System and Chest CT Severity Scores in Patients Suspected of Having COVID-19 Using Artificial Intelligence",
Radiology,
2021;298(1):E18-E28.
J. Bleker, D. Yakar, B. van Noort, D. Rouw, I. de Jong, R. Dierckx, T. Kwee and H. Huisman, "Single-center versus multi-center biparametric MRI radiomics approach for clinically significant peripheral zone prostate cancer",
Insights into Imaging,
2021;12(1).
F. Michallek, H. Huisman, B. Hamm, S. Elezkurtaj, A. Maxeiner and M. Dewey, "Accuracy of fractal analysis and PI-RADS assessment of prostate magnetic resonance imaging for prediction of cancer grade groups: a clinical validation study",
European Radiology,
2021.
T. Perik, E. van Genugten, E. Aarntzen, E. Smit, H. Huisman and J. Hermans, "Quantitative CT perfusion imaging in patients with pancreatic cancer: a systematic review",
Abdominal Radiology,
2021.
A. Saha, M. Hosseinzadeh and H. Huisman, "End-to-end Prostate Cancer Detection in bpMRI via 3D CNNs: Effects of Attention Mechanisms, Clinical Priori and Decoupled False Positive Reduction",
Medical Image Analysis,
2021:102155.
M. Hosseinzadeh, A. Saha, P. Brand, I. Slootweg, M. de Rooij and H. Huisman, "Deep learning-assisted prostate cancer detection on bi-parametric MRI: minimum training data size requirements and effect of prior knowledge",
European Radiology,
2021.
F. Michallek, H. Huisman, B. Hamm, S. Elezkurtaj, A. Maxeiner and M. Dewey, "Prediction of prostate cancer grade using fractal analysis of perfusion MRI: retrospective proof-of-principle study",
European Radiology,
2021.
D. Winkel, A. Tong, B. Lou, A. Kamen, D. Comaniciu, J. Disselhorst, A. Rodr\'ıguez-Ruiz, H. Huisman, D. Szolar, I. Shabunin, M. Choi, P. Xing, T. Penzkofer, R. Grimm, H. von Busch and D. Boll, "A Novel Deep Learning Based Computer-Aided Diagnosis System Improves the Accuracy and Efficiency of Radiologists in Reading Biparametric Magnetic Resonance Images of the Prostate",
Investigative Radiology,
2021;Publish Ahead of Print.
M. Antonelli, A. Reinke, S. Bakas, K. Farahani, AnnetteKopp-Schneider, B. Landman, G. Litjens, B. Menze, O. Ronneberger, R. Summers, B. van Ginneken, M. Bilello, P. Bilic, P. Christ, R. Do, M. Gollub, S. Heckers, H. Huisman, W. Jarnagin, M. McHugo, S. Napel, J. Pernicka, K. Rhode, C. Tobon-Gomez, E. Vorontsov, H. Huisman, J. Meakin, S. Ourselin, M. Wiesenfarth, P. Arbelaez, B. Bae, S. Chen, L. Daza, J. Feng, B. He, F. Isensee, Y. Ji, F. Jia, N. Kim, I. Kim, D. Merhof, A. Pai, B. Park, M. Perslev, R. Rezaiifar, O. Rippel, I. Sarasua, W. Shen, J. Son, C. Wachinger, L. Wang, Y. Wang, Y. Xia, D. Xu, Z. Xu, Y. Zheng, A. Simpson, L. Maier-Hein and M. Cardoso, "The Medical Segmentation Decathlon",
arXiv preprint arXiv:2106.05735,
2021.
J. Bosma, A. Saha, M. Hosseinzadeh, I. Slootweg, M. de Rooij and H. Huisman, "Annotation-efficient cancer detection with report-guided lesion annotation for deep learning-based prostate cancer detection in bpMRI",
arXiv:2112.05151,
2021.
B. de Wilde, R. ten Broek and H. Huisman, "Cine-MRI detection of abdominal adhesions with spatio-temporal deep learning",
Medical Imaging with Deep Learning,
2021.
A. Saha, J. Bosma, J. Linmans, M. Hosseinzadeh and H. Huisman, "Anatomical and Diagnostic Bayesian Segmentation in Prostate MRI -- Should Different Clinical Objectives Mandate Different Loss Functions?",
Medical Imaging Meets NeurIPS Workshop - 35th Conference on Neural Information Processing Systems (NeurIPS),
2021.
A. Saha, J. Bosma, C. Roest, M. Hosseinzadeh, J. Futterer and H. Huisman, "Deep Learning with Bayesian Inference for Prostate Cancer Diagnosis across Longitudinal Biparametric MRI",
Annual Meeting of the Radiological Society of North America,
2021.
N. Alves, J. Hermans and H. Huisman, "CT-based Deep Learning Towards Early Detection Of Pancreatic Ductal Adenocarcinoma",
Annual Meeting of the Radiological Society of North America,
2021.
J. Bosma, A. Saha, M. Hosseinzadeh and H. Huisman, "Augmenting AI with Automated Segmentation of Report Findings Applied to Prostate Cancer Detection in Biparametric MRI",
Annual Meeting of the Radiological Society of North America,
2021.
J. Bosma, A. Saha, M. Hosseinzadeh and H. Huisman, "Augmenting AI with Automated Segmentation of Report Findings Applied to Prostate Cancer Detection in Biparametric MRI",
Master thesis,
2021.
A. Rossi, M. Hosseinzadeh, M. Bianchini, F. Scarselli and H. Huisman, "Multi-modal siamese network for diagnostically similar lesion retrieval in prostate MRI",
IEEE Transactions on Medical Imaging,
2020.
M. Omar, M. Roobol, M. Ribal, T. Abbott, P. Agapow, S. Araujo, A. Asiimwe, C. Auffray, I. Balaur, K. Beyer, C. Bernini, A. Bjartell, A. Briganti, J. Butler-Ransohoff, R. Campi, M. Cavelaars, B. De Meulder, Z. Devecseri, M. Voss, K. Dimitropoulos, S. Evans-Axelsson, B. Franks, L. Fullwood, D. Horgan, E. Smith, A. Kiran, K. Kivinummi, M. Lambrecht, D. Lancet, P. Lindgren, S. MacLennan, S. MacLennan, M. Nogueira, F. Moen, M. Moinat, K. Papineni, C. Reich, K. Reiche, S. Rogiers, C. Sartini, K. van Bochove, F. van Diggelen, M. Van Hemelrijck, H. Van Poppel, J. Zong, J. N'Dow, E. Andersson, H. Arala, A. Auvinen, C. Bangma, D. Burke, A. Cardone, J. Casariego, G. Cuperus, S. Dabestani, F. Esperto, N. Fossati, A. Fridhammar, G. Gandaglia, D. Tandefelt, F. Horn, J. Huber, J. Hugosson, H. Huisman, A. Josefsson, O. Kilkku, M. Kreuz, M. Lardas, J. Lawson, F. Lefresne, S. Lejeune, E. Longden-Chapman, G. McVie, L. Moris, N. Mottet, T. Murtola, C. Nicholls, K. Pang, K. Pascoe, M. Picozzi, K. Plass, P. Pohjanjousi, M. Reaney, S. Remmers, P. Robinson, J. Schalken, M. Schravendeel, T. Seisen, A. Servan, K. Shiranov, R. Snijder, C. Steinbeisser, N. Taibi, K. Talala, D. Tilki, T. den Van Broeck, Z. Vassilev, O. Voima, E. Vradi, R. Waldeck, W. Weistra, P. Willemse, M. Wirth, R. Wolfinger, N. Kermani and T. Consortium, "Introducing PIONEER: a project to harness big data in prostate cancer research",
Nature Reviews Urology,
2020;17:351-362.
T. Boers, Y. Hu, E. Gibson, D. Barratt, E. Bonmati, J. Krdzalic, F. van der Heijden, J. Hermans and H. Huisman, "Interactive 3D U-net for the Segmentation of the Pancreas in Computed Tomography Scans",
Physics in Medicine and Biology,
2020;65(6):065002.
X. Yu, B. Lou, B. Shi, D. Winkel, N. Arrahmane, M. Diallo, T. Meng, H. von Busch, R. Grimm, B. Kiefer, D. Comaniciu, A. Kamen, H. Huisman, A. Rosenkrantz, T. Penzkofer, I. Shabunin, M. Choi, Q. Yang and D. Szolar, "False Positive Reduction Using Multiscale Contextual Features for Prostate Cancer Detection in Multi-Parametric MRI Scans",
IEEE International Symposium on Biomedical Imaging,
2020.
A. Saha, M. Hosseinzadeh and H. Huisman, "Encoding Clinical Priori in 3D Convolutional Neural Networks for Prostate Cancer Detection in bpMRI",
Medical Imaging Meets NeurIPS Workshop - 34th Conference on Neural Information Processing Systems (NeurIPS),
2020.
T. Riepe, M. Hosseinzadeh, P. Brand and H. Huisman, "Anisotropic Deep Learning Multi-planar Automatic Prostate Segmentation",
Annual Meeting of the International Society for Magnetic Resonance in Medicine,
2020.
J. Bleker, T. Kwee, R. Dierckx, I. de Jong, H. Huisman and D. Yakar, "Multiparametric MRI and auto-fixed volume of interest-based radiomics signature for clinically significant peripheral zone prostate cancer",
European Radiology,
2019.
O. Debats, G. Litjens and H. Huisman, "Lymph node detection in MR Lymphography: false positive reduction using multi-view convolutional neural networks",
PeerJ,
2019;7:e8052.
M. Hosseinzadeh, P. Brand and H. Huisman, "Effect of Adding Probabilistic Zonal Prior in Deep Learning-based Prostate Cancer Detection",
Medical Imaging with Deep Learning,
2019.
S. Hu, D. Worrall, S. Knegt, B. Veeling, H. Huisman and M. Welling, "Supervised Uncertainty Quantification for Segmentation with Multiple Annotations",
Lecture Notes in Computer Science,
2019:137-145.
S. Armato, H. Huisman, K. Drukker, L. Hadjiiski, J. Kirby, N. Petrick, G. Redmond, M. Giger, K. Cha, A. Mamonov, J. Kalpathy-Cramer and K. Farahani, "The PROSTATEx Challenges for Computerized Classification of Prostate Lesions from Multi-Parametric Magnetic Resonance Images",
Journal of Medical Imaging,
2018;5(4):044501.
W. Venderink, M. de Rooij, M. Sedelaar, H. Huisman and J. Futterer, "Elastic versus rigid image registration in MRI-TRUS fusion prostate biopsy: a systematic review and meta-analysis",
European Urology Focus,
2018;4:219-227.
E. Gibson, Yipeng, H. Ghavami, H. Ahmed, C. Moore, M. Emberton, H. Huisman and D. Barratt, "Inter-site variability in prostate segmentation accuracy using deep learning",
Medical Image Computing and Computer-Assisted Intervention,
2018.
W. Venderink, M. van der Leest, A. van Luijtelaar, W. van de Ven, J. Futterer, J. Sedelaar and H. Huisman, "Retrospective comparison of direct in-bore magnetic resonance imaging (MRI) guided biopsy and fusion guided biopsy in patients with MRI lesions which are likely or highly likely to be clinically significant prostate cancer",
World Journal of Urology,
2017;35(12):1849-1855.
E. Gibson, Y. Hu, H. Huisman and D. Barratt, "Designing image segmentation studies: statistical power, sample size and reference standard quality",
Medical Image Analysis,
2017;42:44-59.
L. Stoilescu and H. Huisman, "Feasibility of multireferencetissue normalization of T2weighted prostate MRI",
Annual Meeting of the Radiological Society of North America,
2017.
L. Stoilescu, M. Maas and H. Huisman, "Feasibility of multireference tissue normalization of T2-weighted prostate MRI",
European Society for Magnetic Resonance in Medicine and Biology,
2017.
O. Debats, A. Fortuin, H. Meijer, T. Hambrock, G. Litjens, J. Barentsz and H. Huisman, "Intranodal signal suppression in pelvic MR lymphography of prostate cancer patients: a quantitative comparison of ferumoxtran-10 and ferumoxytol",
PeerJ,
2016;4:e2471.
O. Debats, M. Meijs, G. Litjens and H. Huisman, "Automated multistructure atlas-assisted detection of lymph nodes using pelvic MR lymphography in prostate cancer patients",
Medical Physics,
2016;43(6):3132.
W. van de Ven, W. Venderink, J. Sedelaar, J. Veltman, J. Barentsz, J. Fütterer, E. Cornel and H. Huisman, "MR-targeted TRUS prostate biopsy using local reference augmentation: initial experience",
International Urology and Nephrology,
2016.
G. Litjens, R. Elliott, N. Shih, M. Feldman, T. Kobus, C. Hulsbergen-van de Kaa, J. Barentsz, H. Huisman and A. Madabhushi, "Computer-extracted Features Can Distinguish Noncancerous Confounding Disease from Prostatic Adenocarcinoma at Multiparametric MR Imaging.",
Radiology,
2016;278(1):135-145.
W. van de Ven, J. Sedelaar, M. van der Leest, C. de Hulsbergen-van Kaa, J. Barentsz, J. Futterer and H. Huisman, "Visibility of prostate cancer on transrectal ultrasound during fusion with multi-parametric magnetic resonance imaging for biopsy",
Clinical Imaging,
2016;40(4):745-750.
G. Litjens, J. Barentsz, N. Karssemeijer and H. Huisman, "Clinical evaluation of a computer-aided diagnosis system for determining cancer aggressiveness in prostate MRI",
European Radiology,
2015;25(11):3187-3199.
W. van de Ven, Y. Hu, J. Barentsz, N. Karssemeijer, D. Barratt and H. Huisman, "Biomechanical modeling constrained surface-based image registration for prostate MR guided TRUS biopsy",
Medical Physics,
2015;42:2470-2481.
L. Reis\aeter , J. Fütterer, O. Halvorsen, Y. Nyg\r ard, M. Biermann, E. Andersen, K. Gravdal, S. Haukaas, J. Monssen, H. Huisman, L. Akslen, C. Beisland and J. R\orvik , "1.5-T multiparametric MRI using PI-RADS: a region by region analysis to localize the index-tumor of prostate cancer in patients undergoing prostatectomy",
Acta Radiologica,
2015;56:500-511.
M. Meijs, O. Debats and H. Huisman, "The evaluation of multi-structure, multi-atlas pelvic anatomy features in a prostate MR Lymphography CAD system",
Medical Imaging,
2015;9414:94140T.
E. Gibson, H. Huisman and D. Barratt, "Statistical Power in Image Segmentation: Relating Sample Size to Reference Standard Quality",
Lecture Notes in Computer Science,
2015:105-113.
G. Litjens, H. Huisman, R. Elliott, N. Shih, M. Feldman, S. Viswanath, J. Fütterer, J. Bomers and A. Madabhushi, "Quantitative identification of magnetic resonance imaging features of prostate cancer response following laser ablation and radical prostatectomy",
Journal of Medical Imaging,
2014;1(3):035001-035001.
G. Litjens, O. Debats, J. Barentsz, N. Karssemeijer and H. Huisman, "Computer-aided detection of prostate cancer in MRI",
IEEE Transactions on Medical Imaging,
2014;33(5):1083-1092.
G. Litjens, R. Toth, W. van de Ven, C. Hoeks, S. Kerkstra, B. van Ginneken, G. Vincent, G. Guillard, N. Birbeck, J. Zhang, R. Strand, F. Malmberg, Y. Ou, C. Davatzikos, M. Kirschner, F. Jung, J. Yuan, W. Qiu, Q. Gao, P. Edwards, B. Maan, F. van der Heijden, S. Ghose, J. Mitra, J. Dowling, D. Barratt, H. Huisman and A. Madabhushi, "Evaluation of prostate segmentation algorithms for MRI: The PROMISE12 challenge",
Medical Image Analysis,
2014;18(2):359-373.
G. Litjens, R. Elliott, N. Shih, M. Feldman, J. Barentsz, C. - van de Hulsbergen Kaa, I. Kovacs, H. Huisman and A. Madabhushi, "Distinguishing prostate cancer from benign confounders via a cascaded classifier on multi-parametric MRI",
Medical Imaging,
2014;9035:903512.
G. Litjens, H. Huisman, R. Elliott, N. Shih, M. Feldman, Fütterer, J. Bomers and A. Madabhushi, "Distinguishing benign confounding treatment changes from residual prostate cancer on MRI following laser ablation",
Medical Imaging,
2014;9036:90361D.
W. van de Ven, S. Rinsma, N. Karssemeijer, J. Barentsz and H. Huisman, "Electro-magnetic tracker-based fusion for image-guided TRUS prostate biopsy",
European Congress of Radiology,
2014.
G. Litjens, N. Karssemeijer, J. Barentsz and H. Huisman, "Computer-aided Detection of Prostate Cancer in Multi-parametric Magnetic Resonance Imaging",
Annual Meeting of the Radiological Society of North America,
2014.
E. Vos, G. Litjens, T. Kobus, T. Hambrock, C. Kaa, J. Barentsz, H. Huisman and T. Scheenen, "Assessment of Prostate Cancer Aggressiveness Using Dynamic Contrast-enhanced Magnetic Resonance Imaging at 3T",
European Urology,
2013;64:448-455.
T. Tan, B. Platel, R. Mann, H. Huisman and N. Karssemeijer, "Chest Wall Segmentation in Automated 3D Breast Ultrasound Scans",
Medical Image Analysis,
2013;17:1273AC/a,!aEURoe1281.
T. Hambrock, P. Vos, C. de Hulsbergen-van Kaa, J. Barentsz and H. Huisman, "Prostate Cancer: Computer-aided Diagnosis with Multiparametric 3-T MR Imaging--Effect on Observer Performance",
Radiology,
2013;266:521-530.
W. van de Ven, C. de Hulsbergen-van Kaa, T. Hambrock, J. Barentsz and H. Huisman, "Simulated required accuracy of image registration tools for targeting high-grade cancer components with prostate biopsies",
European Radiology,
2013;23(5):1401-1407.
W. van de Ven, Y. Hu, J. Barentsz, N. Karssemeijer, D. Barratt and H. Huisman, "Surface-based prostate registration with biomechanical regularization",
Medical Imaging,
2013;8671:86711R.
G. Litjens, J. Barentsz, N. Karssemeijer and H. Huisman, "Initial prospective evaluation of the prostate imaging reporting and data standard (PI-RADS): Can it reduce unnecessary MR guided biopsies?",
Annual Meeting of the Radiological Society of North America,
2013.
W. van de Ven, N. Karssemeijer, J. Barentsz and H. Huisman, "Image registration for prostate MR guided biopsy using automated biomechanical modeling",
Annual Meeting of the Radiological Society of North America,
2013.
G. Litjens, T. Hambrock, C. de Hulsbergen-van Kaa, J. Barentsz and H. Huisman, "Interpatient Variation in Normal Peripheral Zone Apparent Diffusion Coefficient: Effect on the Prediction of Prostate Cancer Aggressiveness",
Radiology,
2012;265(1):260-266.
M. Stoutjesdijk, M. Zijp, C. Boetes, N. Karssemeijer, J. Barentsz and H. Huisman, "Computer aided analysis of breast MRI enhancement kinetics using mean shift c lustering and multifeature iterative region of interest selection",
Journal of Magnetic Resonance Imaging,
2012;36:1104-1112.
M. Leach, B. Morgan, P. Tofts, D. Buckley, W. Huang, M. Horsfield, T. Chenevert, D. Collins, A. Jackson, D. Lomas, B. Whitcher, L. Clarke, R. Plummer, I. Judson, R. Jones, R. Alonzi, T. Brunner, D. Koh, P. Murphy, J. Waterton, G. Parker, M. Graves, T. Scheenen, T. Redpath, M. Orton, G. Karczmar, H. Huisman, J. Barentsz, A. Padhani and E. on behalf of the Committee, "Imaging vascular function for early stage clinical trials using dynamic contrast-enhanced magnetic resonance imaging",
European Radiology,
2012;22(7):1451-1464.
T. Mertzanidou, J. Hipwell, M. Cardoso, X. Zhang, C. Tanner, S. Ourselin, U. Bick, H. Huisman, N. Karssemeijer and D. Hawkes, "MRI to X-ray mammography registration using a volume-preserving affine transformation",
Medical Image Analysis,
2012;16(5):966-975.
P. Vos, J. Barentsz, N. Karssemeijer and H. Huisman, "Automatic computer-aided detection of prostate cancer based on multiparametric magnetic resonance image analysis",
Physics in Medicine and Biology,
2012;57(6):1527-1542.
T. Tan, B. Platel, H. Huisman, C. Sánchez, R. Mus and N. Karssemeijer, "Computer Aided Lesion Diagnosis in Automated 3D Breast Ultrasound Using Coronal Spiculation",
IEEE Transactions on Medical Imaging,
2012;31(5):1034-1042.
M. Schouten, J. Bomers, D. Yakar, H. Huisman, E. Rothgang, D. Bosboom, T. Scheenen, S. Misra and J. Fütterer, "Evaluation of a robotic technique for transrectal MRI-guided prostate biopsies",
European Radiology,
2012;22:476-483.
T. Hambrock, C. Hoeks, C. de Hulsbergen-van Kaa, T. Scheenen, J. Fütterer, S. Bouwense, I. van Oort, F. Schröder, H. Huisman and J. Barentsz, "Prospective Assessment of Prostate Cancer Aggressiveness Using 3-T Diffusion-Weighted Magnetic Resonance Imaging-Guided Biopsies Versus a Systematic 10-Core Transrectal Ultrasound Prostate Biopsy Cohort",
European Urology,
2012;61(1):177-184.
J. Lesniak, G. van Schie, C. Tanner, B. Platel, H. Huisman, N. Karssemeijer and G. Szekely, "Multimodal Classification of Breast Masses in Mammography and MRI using Unimodal Feature Selection and Decision Fusion",
IWDM '12: Proceedings of the 11th International Workshop on Breast Imaging,
2012;7361:88-95.
G. Litjens, J. Barentsz, N. Karssemeijer and H. Huisman, "Automated computer-aided detection of prostate cancer in MR images: from a whole-organ to a zone-based approach",
Medical Imaging,
2012;8315(1):83150G-83150G-6.
G. Litjens, O. Debats, W. van de Ven, N. Karssemeijer and H. Huisman, "A pattern recognition approach to zonal segmentation of the prostate on MRI",
Medical Image Computing and Computer-Assisted Intervention,
2012;7511:413-420.
G. Litjens, N. Karssemeijer and H. Huisman, "A multi-atlas approach for prostate segmentation in MRI",
MICCAI} {W}orkshop: {P}rostate {C}ancer {I}maging: The {PROMISE12} Prostate Segmentation Challenge,
2012.
G. Litjens, J. Barentsz, N. Karssemeijer and H. Huisman, "Computerized characterization of central gland lesions using texture and relaxation features from T2-weighted prostate MRI",
Annual Meeting of the Radiological Society of North America,
2012.
E. Vos, G. Litjens, T. Kobus, T. Hambrock, C. van de Hulsbergen Kaa, H. Huisman and T. Scheenen, "Dynamic contrast enhanced MR imaging for the assessment of prostate cancer aggressiveness at 3T",
Annual Meeting of the International Society for Magnetic Resonance in Medicine,
2012.
A. Melbourne, J. Hipwell, M. Modat, T. Mertzanidou, H. Huisman, S. Ourselin and D. Hawkes, "The effect of motion correction on pharmacokinetic parameter estimation in dynamic-contrast-enhanced MRI",
Physics in Medicine and Biology,
2011;56(24):7693-7708.
O. Debats, G. Litjens, J. Barentsz, N. Karssemeijer and H. Huisman, "Automated 3-Dimensional Segmentation of Pelvic Lymph Nodes in Magnetic Resonance Images",
Medical Physics,
2011;38(11):6178-6187.
C. Hoeks, J. Barentsz, T. Hambrock, D. Yakar, D. Somford, S. Heijmink, T. Scheenen, P. Vos, H. Huisman, I. van Oort, J. Witjes, A. Heerschap and J. Fütterer, "Prostate Cancer: Multiparametric MR Imaging for Detection, Localization, and Staging",
Radiology,
2011;261(1):46-66.
R. Mann, J. Veltman, H. Huisman and C. Boetes, "Comparison of enhancement characteristics between invasive lobular carcinoma and invasive ductal carcinoma",
Journal of Magnetic Resonance Imaging,
2011;34(2):293-300.
M. Nillesen, R. Lopata, H. Huisman, J. Thijssen, L. Kapusta and C. de Korte, "Correlation based 3-D segmentation of the left ventricle in pediatric echocardiographic images using radio-frequency data",
Ultrasound in Medicine and Biology,
2011;37(9):1409-1420.
T. Hambrock, D. Somford, H. Huisman, I. van Oort, J. Witjes, C. de Hulsbergen-van Kaa, T. Scheenen and J. Barentsz, "Relationship between Apparent Diffusion Coefficients at 3.0-T MR Imaging and Gleason Grade in Peripheral Zone Prostate Cancer",
Radiology,
2011;259(2):453-461.
D. Yakar, S. Heijmink, C. de Hulsbergen-van Kaa, H. Huisman, J. Barentsz, J. Fütterer and T. Scheenen, "Initial results of 3-dimensional 1H-magnetic resonance spectroscopic imaging in the localization of prostate cancer at 3 Tesla: should we use an endorectal coil?",
Investigative Radiology,
2011;46(5):301-306.
B. Platel, H. Huisman, H. Laue, R. Mus, R. Mann, H. Hahn and N. Karssemeijer, "Computerized Characterization of Breast Lesions using Dual-Temporal Resolution Dynamic Contrast-Enhanced MR Images",
MICCAI} {W}orkshop: {B}reast {I}mage {A}nalysis,
2011.
W. van de Ven, G. Litjens, J. Barentsz, T. Hambrock and H. Huisman, "Required accuracy of MR-US registration for prostate biopsies",
P}rostate {C}ancer {I}maging. {I}mage {A}nalysis and {I}mage-{G}uided {I}nterventions,
2011;6963:92-99.
G. Litjens, P. Vos, J. Barentsz, N. Karssemeijer and H. Huisman, "Automatic Computer Aided Detection of Abnormalities in Multi-Parametric Prostate MRI",
Medical Imaging,
2011;7963(1).
T. Tan, B. Platel, H. Huisman and N. Karssemeijer, "Chest wall segmentation in automated 3D breast ultrasound using a cylinder model",
MICCAI} {W}orkshop: {B}reast {I}mage {A}nalysis,
2011.
T. Tan, H. Huisman, B. Platel, A. Grivignee, R. Mus and N. Karssemeijer, "Classification of Breast Lesions in Automated 3D Breast Ultrasound",
Medical Imaging,
2011;7963:79630X.
N. Karssemeijer, T. Tan, B. Platel, T. Twellmann, L. Tabar, A. Grivignee, R. Mus and H. Huisman, "A Novel System for Computer-aided Lesion Classification in Automated 3D Breast Ultrasound",
Annual Meeting of the Radiological Society of North America,
2011.
G. Litjens, J. Barentsz, N. Karssemeijer and H. Huisman, "Zone-specific Automatic Computer-aided Detection of Prostate Cancer in MRI",
Annual Meeting of the Radiological Society of North America,
2011.
O. Debats, T. Hambrock, G. Litjens, H. Huisman and J. Barentsz, "Detection of Lymph Node Metastases with Ferumoxtran-10 vs Ferumoxytol",
Annual Meeting of the Radiological Society of North America,
2011.
B. Platel, H. Huisman, H. Laue, R. Mann, H. Hahn, N. Karssemeijer and R. Mus, "Computerized Characterization of Breast Masses Using Dual-Temporal Resolution Dynamic Contrast-enhanced MR Images",
Annual Meeting of the Radiological Society of North America,
2011.
P. Vos, T. Hambrock, J. Barentsz and H. Huisman, "Computer-assisted analysis of peripheral zone prostate lesions using T2-weighted and dynamic contrast enhanced T1-weighted MRI",
Physics in Medicine and Biology,
2010;55(6):1719-1734.
D. Yakar, T. Hambrock, H. Huisman, C. de Hulsbergen-van Kaa, E. van Lin, H. Vergunst, C. Hoeks, I. van Oort, J. Witjes, J. Barentsz and J. Fütterer, "Feasibility of 3T dynamic contrast-enhanced magnetic resonance-guided biopsy in localizing local recurrence of prostate cancer after external beam radiation therapy",
Investigative Radiology,
2010;45(3):121-125.
E. Tanck, J. Deenen, H. Huisman, J. Kooloos, H. Huizenga and N. Verdonschot, "An anatomically shaped lower body model for CT scanning of cadaver femurs",
Physics in Medicine and Biology,
2010;55(2):N57-N62.
T. Hambrock, D. Somford, C. Hoeks, S. Bouwense, H. Huisman, D. Yakar, I. van Oort, J. Witjes, J. Fütterer and J. Barentsz, "Magnetic resonance imaging guided prostate biopsy in men with repeat negative biopsies and increased prostate specific antigen",
Journal of Urology,
2010;183(2):520-527.
H. Huisman, P. Vos, G. Litjens, T. Hambrock and J. Barentsz, "Computer aided detection of prostate cancer using t2w, DWI and DCE-MRI: methods and clinical applications",
MICCAI} {W}orkshop: {P}rostate {C}ancer {I}maging: {C}omputer {A}ided {D}iagnosis, {P}rognosis, and {I}ntervention,
2010.
A. Makarau, H. Huisman, R. Mus, M. Zijp and N. Karssemeijer, "Breast MRI intensity non-uniformity correction using mean-shift",
Medical Imaging,
2010;7624:76242D.
O. Debats, N. Karssemeijer, J. Barentsz and H. Huisman, "Automated classification of lymph nodes in USPIO-enhanced MR-images: a comparison of three segmentation methods",
Medical Imaging,
2010;7624:76240Q.
H. Huisman, J. Veltman, M. Zijp, R. Mann, R. Mus and N. Karssemeijer, "Dual-Time Resolution Characterization of Masses on Breast DCEMR",
Annual Meeting of the Radiological Society of North America,
2010.
C. Hoeks, J. Fütterer, D. Somford, I. van Oort, H. Huisman and J. Barentsz, "Multiparametric MRI for prostate cancer screening",
Nederlands Tijdschrift voor Geneeskunde,
2009;153:B487.
M. Nillesen, R. Lopata, W. de Boode, I. Gerrits, H. Huisman, J. Thijssen, L. Kapusta and C. de Korte, "In vivo validation of cardiac output assessment in non-standard 3D echocardiographic images",
Physics in Medicine and Biology,
2009;54(7):1951-1962.
C. van Niekerk, J. van der Laak, M. Börger, H. Huisman, J. Witjes, J. Barentsz and C. de Hulsbergen-van Kaa, "Computerized whole slide quantification shows increased microvascular density in pT2 prostate cancer as compared to normal prostate tissue",
Prostate,
2009;69(1):62-69.
M. Nillesen, R. Lopata, H. Huisman, J. Thijssen, L. Kapusta and C. de Korte, "3D cardiac segmentation using temporal correlation of radio frequency ultrasound data",
Medical Image Computing and Computer-Assisted Intervention,
2009;12:927-934.
P. Vos, T. Hambrock, J. Barentsz and H. Huisman, "Automated calibration for computerized analysis of prostate lesions using pharmacokinetic magnetic resonance images",
Medical Image Computing and Computer-Assisted Intervention,
2009;12:836-843.
T. Hambrock, J. Fütterer, H. Huisman, C. de Hulsbergen-van Kaa, J. van Basten, I. van Oort, J. Witjes and J. Barentsz, "Thirty-two-channel coil 3T magnetic resonance-guided biopsies of prostate tumor suspicious regions identified on multimodality 3T magnetic resonance imaging: technique and feasibility",
Investigative Radiology,
2008;43(10):686-694.
P. Vos, T. Hambrock, C. de Kaa, J. Fütterer, J. Barentsz and H. Huisman, "Computerized analysis of prostate lesions in the peripheral zone using dynamic contrast enhanced MRI",
Medical Physics,
2008;35(3):888-899.
J. Veltman, M. Stoutjesdijk, R. Mann, H. Huisman, J. Barentsz, J. Blickman and C. Boetes, "Contrast-enhanced magnetic resonance imaging of the breast: the value of pharmacokinetic parameters derived from fast dynamic imaging during initial enhancement in classifying lesions",
European Radiology,
2008;18(6):1123-1133.
P. Vos, T. Hambrock, J. Barentsz and H. Huisman, "Combining T2-weighted with dynamic MR images for computerized classification of prostate lesions",
Medical Imaging: Computer-Aided Diagnosis,
2008;6915.
M. Stoutjesdijk, J. Veltman, H. Huisman, N. Karssemeijer, J. Barentsz, J. Blickman and C. Boetes, "Automated analysis of contrast enhancement in breast MRI lesions using mean shift clustering for ROI selection",
Journal of Magnetic Resonance Imaging,
2007;26(3):606-614.
M. Nillesen, R. Lopata, I. Gerrits, L. Kapusta, H. Huisman, J. Thijssen and C. de Korte, "Segmentation of the heart muscle in 3-D pediatric echocardiographic images",
Ultrasound in Medicine and Biology,
2007;33(9):1453-1462.
W. Vogel, J. van Dalen, B. Wiering, H. Huisman, F. Corstens, T. Ruers and W. Oyen, "Evaluation of image registration in PET/CT of the liver and recommendations for optimized imaging",
Journal of Nuclear Medicine,
2007;48(6):910-919.
S. Heijmink, J. Fütterer, T. Hambrock, S. Takahashi, T. Scheenen, H. Huisman, C. de Hulsbergen-Van Kaa, B. Knipscheer, L. Kiemeney, J. Witjes and J. Barentsz, "Prostate cancer: body-array versus endorectal coil MR imaging at 3 T--comparison of image quality, localization, and staging performance",
Radiology,
2007;244(1):184-195.
J. Fütterer, T. Scheenen, S. Heijmink, H. Huisman, C. de Kaa, J. Witjes, A. Heerschap and J. Barentsz, "Standardized threshold approach using three-dimensional proton magnetic resonance spectroscopic imaging in prostate cancer localization of the entire prostate",
Investigative Radiology,
2007;42(2):116-122.
H. Huisman and N. Karssemeijer, "Chestwall segmentation in 3D breast ultrasound using a deformable volume model",
Information Processing in Medical Imaging,
2007:245-256.
P. Vos, T. Hambrock, J. Fütterer, C. De Hulsbergen-van Kaa, J. Barentsz and H. Huisman, "Effect of calibration on computerized analysis of prostate lesions using quantitative dynamic contrast-enhanced magnetic resonance imaging",
Medical Imaging: Computer-Aided Diagnosis,
2007;6514.
J. Fütterer, S. Heijmink, T. Scheenen, J. Veltman, H. Huisman, P. Vos, C. Hulsbergen-van de Kaa, J. Witjes, P. Krabbe, A. Heerschap and J. Barentsz, "Prostate cancer localization with dynamic contrast-enhanced MR imaging and proton MR spectroscopic imaging",
Radiology,
2006;241(2):449-458.
E. van Lin, J. Fütterer, S. Heijmink, L. van der Vight, A. Hoffmann, P. van Kollenburg, H. Huisman, T. Scheenen, J. Witjes, J. Leer, J. Barentsz and A. Visser, "IMRT boost dose planning on dominant intraprostatic lesions: gold marker-based three-dimensional fusion of CT with dynamic contrast-enhanced and 1H-spectroscopic MRI",
International Journal of Radiation Oncology, Biology, Physics,
2006;65(1):291-303.
S. van Engeland, P. Snoeren, H. Huisman, C. Boetes and N. Karssemeijer, "Volumetric breast density estimation from full-field digital mammograms",
IEEE Transactions on Medical Imaging,
2006;25(3):273-282.
J. Fütterer, M. Engelbrecht, H. Huisman, G. Jager, C. de Hulsbergen-van Kaa, J. Witjes and J. Barentsz, "Staging prostate cancer with dynamic contrast-enhanced endorectal MR imaging prior to radical prostatectomy: experienced versus less experienced readers",
Radiology,
2005;237(2):541-549.
H. Huisman, J. Fütterer, E. van Lin, A. Welmers, T. Scheenen, J. van Dalen, A. Visser, J. Witjes and J. Barentsz, "Prostate cancer: precision of integrating functional MR imaging with radiation therapy treatment by using fiducial gold markers",
Radiology,
2005;236(1):311-317.
W. Vogel, J. van Dalen, H. Huisman, W. Oyen and N. Karssemeijer, "Sliced alternating DICOM series: convenient visualisation of image fusion on PACS",
European Journal of Nuclear Medicine and Molecular Imaging,
2005;32(2):247-248.
E. van Lin, L. van der Vight, J. Witjes, H. Huisman, J. Leer and A. Visser, "The effect of an endorectal balloon and off-line correction on the interfraction systematic and random prostate position variations: a comparative study",
International Journal of Radiation Oncology, Biology, Physics,
2005;61(1):278-288.
T. Veninga, H. Huisman, R. van der Maazen and H. Huizenga, "Clinical validation of the normalized mutual information method for registration of CT and MR images in radiotherapy of brain tumors",
Journal of Applied Clinical Medical Physics,
2004;5(3):66-79.
J. van Dalen, W. Vogel, H. Huisman, W. Oyen, G. Jager and N. Karssemeijer, "Accuracy of rigid CT-FDG-PET image registration of the liver",
Physics in Medicine and Biology,
2004;49(23):5393-5405.
J. Fütterer, T. Scheenen, H. Huisman, D. Klomp, F. van Dorsten, C. de Hulsbergen-van Kaa, J. Witjes, A. Heerschap and J. Barentsz, "Initial experience of 3 tesla endorectal coil magnetic resonance imaging and 1H-spectroscopic imaging of the prostate",
Investigative Radiology,
2004;39(11):671-680.
M. Engelbrecht, H. Huisman, R. Laheij, G. Jager, G. van Leenders, C. de Hulsbergen-van Kaa, J. de la Rosette, J. Blickman and J. Barentsz, "Discrimination of prostate cancer from normal peripheral zone and central gland tissue by using dynamic contrast-enhanced MR imaging",
Radiology,
2003;229(1):248-254.
H. Huisman, M. Engelbrecht and J. Barentsz, "Accurate estimation of pharmacokinetic contrast-enhanced dynamic MRI parameters of the prostate",
Journal of Magnetic Resonance Imaging,
2001;13(4):607-614.
E. Boss, L. Massuger, L. Pop, L. Verhoef, H. Huisman, H. Boonstra and J. Barentsz, "Post-radiotherapy contrast enhancement changes in fast dynamic MRI of cervical carcinoma",
Journal of Magnetic Resonance Imaging,
2001;13(4):600-606.
H. Huisman, M. Engelbrecht and J. Barentsz, "Accurate estimation of pharmacokinetic contrast-enhanced dynamic MRI parameters of the prostate",
Journal of Magnetic Resonance Imaging,
2001;13(4):607-614.
J. Barentsz, M. Engelbrecht, G. Jager, J. Witjes, J. de LaRosette, B. van Der Sanden, H. Huisman and A. Heerschap, "Fast dynamic gadolinium-enhanced MR imaging of urinary bladder and prostate cancer",
Journal of Magnetic Resonance Imaging,
1999;10(3):295-304.
H. Huisman and J. Thijssen, "An in vivo ultrasonic model of liver parenchyma",
IEEE Transactions on Ultrasonics Ferroelectrics and Frequency Control,
1998;45(3):739-750.
H. Huisman and J. Thijssen, "Adaptive texture feature extraction with application to ultrasonic image analysis",
Ultrasonic Imaging,
1998;20(2):132-148.
H. Huisman, J. Thijssen, D. Wagener and G. Rosenbusch, "Quantitative ultrasonic analysis of liver metastases",
Ultrasound in Medicine and Biology,
1998;24(1):67-77.
A. Berkhoff, H. Huisman, J. Thijssen, E. Jacobs and R. Homan, "Fast scan conversion algorithms for displaying ultrasound sector images",
Ultrasonic Imaging,
1994;16(2):87-108.