Henkjan Huisman

Radboud University Nijmegen

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Co-Authors

Jelle O. Barentsz

Andros Mannenkliniek

Jurgen J. Fütterer

Radboud University Nijmegen

Geert Litjens

Radboud University Medical Center

Derya Yakar

University Medical Center Groningen

Thomas Hambrock

Radboud University Nijmegen

Anindo Saha

Radboud University Nijmegen

Maarten de Rooij

Radboud University Nijmegen

Dean C. Barratt

Wellcome / EPSRC Centre for Interventional and Surgical Sciences

Joeran S. Bosma

Radboud University Medical Center

Bram van Ginneken

University Medical Center

Cooperative Institutions

Radboud University Nijmegen

219

Radboud University Medical Center

200

University Medical Center

102

University College London

Tampere University

University of Amsterdam

University Medical Center Utrecht

Heidelberg University

European Association of Urology

Erasmus MC

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HAMAM – Novel Software Tools for Efficient Assessment of Multi-Modal Breast Imaging

Thorsten Twellmann Nico Karssemeijer Fabian Zöhrer John H. Hipwell Christine Tanner

Poster: ECR 2012 / C-2611 / HAMAM – Novel Software Tools for Efficient Assessment of Multi-Modal Breast Imaging by: Twellmann1, N. Karssemeijer2, F. Zohrer1, J. Hipwell3, C. Tanner4, J. Plath1, T. Bohler1, H. Huisman2, T. Mertzanidou3, R. Blanc4, M. T. Harz1, J. M. Lesniak4, J. Berg5, U. Bick6, K. Schilling7, D. J. Hawkes3, G. Szekely4, M. Hierath8, H. Hahn1; 1Bremen/DE, 2Nijmegen/NL, 3London/UK, 4Zuerich/CH, 5Dundee/UK, 6Berlin/DE, 7Boca Raton, FL/US, 8Vienna/AT

Breast imaging

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Automated Calibration for Computerized Analysis of Prostate Lesions Using Pharmacokinetic Magnetic Resonance Images

Lecture notes in computer science (2009)

Pieter Vos Thomas Hambrock Jelle O. Barenstz Henkjan Huisman

10.1007/978-3-642-04271-3_101

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Supervised Uncertainty Quantification for Segmentation with Multiple Annotations

arXiv (Cornell University) (2019)

Hu Shi Daniel E. Worrall Stefan J. L. Knegt Bas Veeling Henkjan Huisman

The accurate estimation of predictive uncertainty carries importance in medical scenarios such as lung node segmentation. Unfortunately, most existing works on predictive uncertainty do not return calibrated uncertainty estimates, which could be used in practice. In this work we exploit multi-grader annotation variability as a source of 'groundtruth' aleatoric uncertainty, which can be treated as a target in a supervised learning problem. We combine this groundtruth uncertainty with a Probabilistic U-Net and test on the LIDC-IDRI lung nodule CT dataset and MICCAI2012 prostate MRI dataset. We find that we are able to improve predictive uncertainty estimates. We also find that we can improve sample accuracy and sample diversity. In real-world applications, our method could inform doctors about the confidence of the segmentation results.

Sample (material)

Uncertainty Quantification

10.48550/arxiv.1907.01949

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PROSTATEx Challenges for computerized classification of prostate lesions from multiparametric magnetic resonance images

Journal of Medical Imaging (2018)

Samuel G. Armato Henkjan Huisman Karen Drukker Lubomir M. Hadjiiski Justin Kirby

Grand challenges stimulate advances within the medical imaging research community; within a competitive yet friendly environment, they allow for a direct comparison of algorithms through a well-defined, centralized infrastructure. The tasks of the two-part PROSTATEx Challenges (the PROSTATEx Challenge and the PROSTATEx-2 Challenge) are (1) the computerized classification of clinically significant prostate lesions and (2) the computerized determination of Gleason Grade Group in prostate cancer, both based on multiparametric magnetic resonance images. The challenges incorporate well-vetted cases for training and testing, a centralized performance assessment process to evaluate results, and an established infrastructure for case dissemination, communication, and result submission. In the PROSTATEx Challenge, 32 groups apply their computerized methods (71 methods total) to 208 prostate lesions in the test set. The area under the receiver operating characteristic curve for these methods in the task of differentiating between lesions that are and are not clinically significant ranged from 0.45 to 0.87; statistically significant differences in performance among the top-performing methods, however, are not observed. In the PROSTATEx-2 Challenge, 21 groups apply their computerized methods (43 methods total) to 70 prostate lesions in the test set. When compared with the reference standard, the quadratic-weighted kappa values for these methods in the task of assigning a five-point Gleason Grade Group to each lesion range from −0.24 to 0.27; superiority to random guessing can be established for only two methods. When approached with a sense of commitment and scientific rigor, challenges foster interest in the designated task and encourage innovation in the field.

Task group

Data set

10.1117/1.jmi.5.4.044501

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Artificial Intelligence and Radiologists at Prostate Cancer Detection in MRI: The PI-CAI Challenge (Study Protocol)

Zenodo (CERN European Organization for Nuclear Research) (2022)

Anindo Saha Jasper Jonathan Twilt Joeran Sander Bosma Bram van Ginneken Derya Yakar

10.5281/zenodo.6667655

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Anatomical and Diagnostic Bayesian Segmentation in Prostate MRI - Should Different Clinical Objectives Mandate Different Loss Functions?

arXiv (Cornell University) (2021)

Anindo Saha Joeran S. Bosma Jasper Linmans Matin Hosseinzadeh Henkjan Huisman

We hypothesize that probabilistic voxel-level classification of anatomy and malignancy in prostate MRI, although typically posed as near-identical segmentation tasks via U-Nets, require different loss functions for optimal performance due to inherent differences in their clinical objectives. We investigate distribution, region and boundary-based loss functions for both tasks across 200 patient exams from the publicly-available ProstateX dataset. For evaluation, we conduct a thorough comparative analysis of model predictions and calibration, measured with respect to multi-class volume segmentation of the prostate anatomy (whole-gland, transitional zone, peripheral zone), as well as, patient-level diagnosis and lesion-level detection of clinically significant prostate cancer. Notably, we find that distribution-based loss functions (in particular, focal loss) are well-suited for diagnostic or panoptic segmentation tasks such as lesion detection, primarily due to their implicit property of inducing better calibration. Meanwhile, (with the exception of focal loss) both distribution and region/boundary-based loss functions perform equally well for anatomical or semantic segmentation tasks, such as quantification of organ shape, size and boundaries.

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Single-center versus multi-center biparametric MRI radiomics approach for clinically significant peripheral zone prostate cancer

Insights into Imaging (2021)

Jeroen Bleker Derya Yakar Bram van Noort Dennis B. Rouw Igle J. de Jong

Abstract Objectives To investigate a previously developed radiomics-based biparametric magnetic resonance imaging (bpMRI) approach for discrimination of clinically significant peripheral zone prostate cancer (PZ csPCa) using multi-center, multi-vendor (McMv) and single-center, single-vendor (ScSv) datasets. Methods This study’s starting point was a previously developed ScSv algorithm for PZ csPCa whose performance was demonstrated in a single-center dataset. A McMv dataset was collected, and 262 PZ PCa lesions (9 centers, 2 vendors) were selected to identically develop a multi-center algorithm. The single-center algorithm was then applied to the multi-center dataset (single–multi-validation), and the McMv algorithm was applied to both the multi-center dataset (multi–multi-validation) and the previously used single-center dataset (multi–single-validation). The areas under the curve (AUCs) of the validations were compared using bootstrapping. Results Previously the single–single validation achieved an AUC of 0.82 (95% CI 0.71–0.92), a significant performance reduction of 27.2% compared to the single–multi-validation AUC of 0.59 (95% CI 0.51–0.68). The new multi-center model achieved a multi–multi-validation AUC of 0.75 (95% CI 0.64–0.84). Compared to the multi–single-validation AUC of 0.66 (95% CI 0.56–0.75), the performance did not decrease significantly ( p value: 0.114). Bootstrapped comparison showed similar single-center performances and a significantly different multi-center performance ( p values: 0.03, 0.012). Conclusions A single-center trained radiomics-based bpMRI model does not generalize to multi-center data. Multi-center trained radiomics-based bpMRI models do generalize, have equal single-center performance and perform better on multi-center data.

Single Center

Center (category theory)

10.1186/s13244-021-01099-y

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Citations (16)

Cardiac output estimation in non-standard 3D echocardiographic images

Maartje M. Nillesen Richard G. P. Lopata Willem P. de Boode I. H. Gerrits Henkjan Huisman

The aim of this study was to validate cardiac output (CO) values obtained by fully automated segmentation of the left ventricle (LV) against CO measured from the volume flow in the pulmonary artery. Segmentation of the LV in 3D echographic images may substantially support clinical diagnosis of (congenital) heart disease. Assumptions about shape and appearance of the heart are often incorporated in the segmentation method. This is advantageous when analyzing echographic images of normal heart geometries in standardized (apical) views. In abnormal heart geometries, for example in children with congenital malformations, this a priori knowledge about the shape and anatomy of the LV will result in erroneous segmentation results. Therefore, we developed an automated segmentation method without using a priori knowledge. Echocardiographic image sequences of five piglets were obtained in radiofrequency (rf) format. Cardiac blood flow was measured simultaneously in the pulmonary artery by an ultrasound flow probe. Three-dimensional adaptive filtering was performed on the demodulated rf-data to optimize the distinction between blood and myocardium. A 3D gradient-based deformable simplex mesh was then used to segment the endocardial surface. The method can be applied to non-standard heart geometries without having to impose shape constraints. Assuming the CO measurements from the flow probe as a gold standard, excellent correlation (r = 0.99) was observed with the CO estimates obtained from segmentation.

Gold standard (test)

10.1109/ultsym.2008.0428

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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 (2004)

Emile N.J.T. van Lin Lisette P. van der Vight J. Alfred Witjes Henkjan Huisman J.W.H. Leer

Isocenter

Fiducial marker

10.1016/j.ijrobp.2004.09.042

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Citations (97)