Paul Suetens
PSI
Kasteelpark Arenberg 10 - box 2441
3001 Leuven
Belgium
room: 01.07
tel: +32 16 32 10 66 or +32 16 32 17 13
contact
Kasteelpark Arenberg 10 - box 2441
3001 Leuven
Belgium
room: 01.07
tel: +32 16 32 10 66 or +32 16 32 17 13
contact
query=user:U0015195 year:[2003 TO 2023] &institution=lirias&from=1&step=20&sort=scdate
showing 1 to 20 of 506
Type
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Bruffaerts, Rose; Gors, Dorothy; Bárcenas Gallardo, Alicia; Vandenbulcke, Mathieu; Van Damme, Philip; Suetens, Paul; van Swieten, John; Borroni, Barbara; Sanchez-Valle, Raquel; Moreno, Fermin; Laforce, Robert; Graff, Caroline; Synofzik, Matthis; Galimberti, Daniela; Rowe, James; Masellis, Mario; Carmela Tartaglia, Maria; Finger, Elizabeth; de Mendonca, Alexandre; Tagliavini, Fabrizio; Butler, Chris; Santana, Isabel; Gerhard, Alexander; Ducharme, Simon; Levin, Johannes; Danek, Adrian; Otto, Markus; Rohrer, Jonathan; Dupont, Patrick; Claes, Peter; Vandenberghe, Rik; Genetic Frontotemporal dementia Initiative (GENFI), ;
2022.
Hierarchical spectral clustering reveals brain size and shape changes in asymptomatic carriers of C9orf72.
Brain Communications; 2022; Vol. 4; iss. 4; pp.
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LIRIAS3775049
description
Traditional methods for detecting asymptomatic brain changes in neurodegenerative diseases such as Alzheimer's disease or frontotemporal degeneration typically evaluate changes in volume at a predefined level of granularity, e.g. voxel-wise or in a priori defined cortical volumes of interest. Here, we apply a method based on hierarchical spectral clustering, a graph-based partitioning technique. Our method uses multiple levels of segmentation for detecting changes in a data-driven, unbiased, comprehensive manner within a standard statistical framework. Furthermore, spectral clustering allows for detection of changes in shape along with changes in size. We performed tensor-based morphometry to detect changes in the Genetic Frontotemporal dementia Initiative asymptomatic and symptomatic frontotemporal degeneration mutation carriers using hierarchical spectral clustering and compared the outcome to that obtained with a more conventional voxel-wise tensor- and voxel-based morphometric analysis. In the symptomatic groups, the hierarchical spectral clustering-based method yielded results that were largely in line with those obtained with the voxel-wise approach. In asymptomatic C9orf72 expansion carriers, spectral clustering detected changes in size in medial temporal cortex that voxel-wise methods could only detect in the symptomatic phase. Furthermore, in the asymptomatic and the symptomatic phases, the spectral clustering approach detected changes in shape in the premotor cortex in C9orf72. In summary, the present study shows the merit of hierarchical spectral clustering for data-driven segmentation and detection of structural changes in the symptomatic and asymptomatic stages of monogenic frontotemporal degeneration.
Publisher: Oxford University Press
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De Buck, Stijn; Van De Bruaene, Alexander; Budts, Werner; Suetens, Paul;
2022.
MeVisLab-OpenVR prototyping platform for virtual reality medical applications.
International Journal Of Computer Assisted Radiology And Surgery; 2022; Vol. 17; iss. 11; pp. 2065 - 2069
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LIRIAS3759417
description
PURPOSE: Virtual reality (VR) can provide an added value for diagnosis and/or intervention planning. Several VR software implementations have been proposed but they are often application dependent. Previous attempts for a more generic solution incorporating VR in medical prototyping software (MeVisLab) were still lacking functionality precluding easy and flexible development. METHODS: We propose an alternative solution that uses rendering to a graphical processing unit (GPU) texture to enable rendering arbitrary Open Inventor scenes in a VR context. It facilitates flexible development of user interaction and rendering of more complex scenes involving multiple objects. We tested the platform in planning a transcatheter cardiac stent placement procedure. RESULTS: This approach proved to enable development of a particular implementation that facilitates planning of percutaneous treatment of a sinus venosus atrial septal defect. The implementation showed it is intuitive to plan and verify the procedure using VR. CONCLUSION: An alternative implementation for linking OpenVR with MeVisLab is provided that offers more flexible development of VR prototypes which can facilitate further clinical validation of this technology in various medical disciplines.
Publisher: Springer Verlag
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Wouters, Anke; Robben, David; Christensen, Soren; Marquering, Henk A; Roos, Yvo BWEM; van Oostenbrugge, Robert J; van Zwam, Wim H; Dippel, Diederik WJ; Majoie, Charles BLM; Schonewille, Wouter J; van Der Lugt, Aad; Lansberg, Maarten; Albers, Gregory W; Suetens, Paul; Lemmens, Robin;
2022.
Prediction of Stroke Infarct Growth Rates by Baseline Perfusion Imaging.
Stroke; 2022; Vol. 53; iss. 2; pp. 569 - 577
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LIRIAS3585746
description
BACKGROUND AND PURPOSE: Computed tomography perfusion imaging allows estimation of tissue status in patients with acute ischemic stroke. We aimed to improve prediction of the final infarct and individual infarct growth rates using a deep learning approach. METHODS: We trained a deep neural network to predict the final infarct volume in patients with acute stroke presenting with large vessel occlusions based on the native computed tomography perfusion images, time to reperfusion and reperfusion status in a derivation cohort (MR CLEAN trial [Multicenter Randomized Clinical Trial of Endovascular Treatment for Acute Ischemic Stroke in the Netherlands]). The model was internally validated in a 5-fold cross-validation and externally in an independent dataset (CRISP study [CT Perfusion to Predict Response to Recanalization in Ischemic Stroke Project]). We calculated the mean absolute difference between the predictions of the deep learning model and the final infarct volume versus the mean absolute difference between computed tomography perfusion imaging processing by RAPID software (iSchemaView, Menlo Park, CA) and the final infarct volume. Next, we determined infarct growth rates for every patient. RESULTS: We included 127 patients from the MR CLEAN (derivation) and 101 patients of the CRISP study (validation). The deep learning model improved final infarct volume prediction compared with the RAPID software in both the derivation, mean absolute difference 34.5 versus 52.4 mL, and validation cohort, 41.2 versus 52.4 mL (P< /0.01). We obtained individual infarct growth rates enabling the estimation of final infarct volume based on time and grade of reperfusion. CONCLUSIONS: We validated a deep learning-based method which improved final infarct volume estimations compared with classic computed tomography perfusion imaging processing. In addition, the deep learning model predicted individual infarct growth rates which could enable the introduction of tissue clocks during the management of acute stroke.
Publisher: American Heart Association
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presentationWouters, Anke; Robben, David; Christensen, Soren; Marquering, Henk; Roos, Yvo; Oostenbrugge, Robert Van V; van Zwam, Wim; Dippel, Diederik W; Majoie, Charles B; van der Lugt, Aad; Lansberg, Maarten G; Albers, Gregory W; Suetens, Paul; Lemmens, Robin; 2021. Prediction of Stroke Lesion Growth Rates by Baseline Perfusion Imaging. Stroke; 2021; Vol. 52; pp. Publisher: American Heart AssociationLIRIAS3522046
description
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Bertels, Jeroen; Robben, David; Vandermeulen, Dirk; Suetens, Paul;
2021.
Theoretical analysis and experimental validation of volume bias of soft Dice optimized segmentation maps in the context of inherent uncertainty.
Medical Image Analysis; 2021; Vol. 67; pp.
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LIRIAS3244743
description
The clinical interest is often to measure the volume of a structure, which is typically derived from a segmentation. In order to evaluate and compare segmentation methods, the similarity between a segmentation and a predefined ground truth is measured using popular discrete metrics, such as the Dice score. Recent segmentation methods use a differentiable surrogate metric, such as soft Dice, as part of the loss function during the learning phase. In this work, we first briefly describe how to derive volume estimates from a segmentation that is, potentially, inherently uncertain or ambiguous. This is followed by a theoretical analysis and an experimental validation linking the inherent uncertainty to common loss functions for training CNNs, namely cross-entropy and soft Dice. We find that, even though soft Dice optimization leads to an improved performance with respect to the Dice score and other measures, it may introduce a volume bias for tasks with high inherent uncertainty. These findings indicate some of the method's clinical limitations and suggest doing a closer ad-hoc volume analysis with an optional re-calibration step.
Publisher: Elsevier
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Berenguer, Abel Díaz; Sahli, Hichem; Joukovsky, Boris; Kvasnytsia, Maryna; Dirks, Ine; Alioscha-Pérez, Mitchel; Deligiannis, Nikos; Gonidakis, Panagiotis; Sánchez, Sebastián Amador; Brahimetaj, Redona; Papavasileiou, Evgenia; Chan, Jonathan Cheung-Wai; Li, Fei; Song, Shangzhen; Yang, Yixin; Tilborghs, Sofie; Willems, Siri; Eelbode, Tom; Bertels, Jeroen; Vandermeulen, Dirk; Maes, Frederik; Suetens, Paul; Fidon, Lucas; Vercauteren, Tom; Robben, David; Brys, Arne; Smeets, Dirk; Ilsen, Bart; Buls, Nico; Watté, Nina; Mey, Johan de; Snoeckx, Annemiek; Parizel, Paul M; Guiot, Julien; Deprez, Louis; Meunier, Paul; Gryspeerdt, Stefaan; Smet, Kristof De; Jansen, Bart; Vandemeulebroucke, Jef;
2020.
Explainable-by-design Semi-Supervised Representation Learning for COVID-19 Diagnosis from CT Imaging..
arXiv; 2020
LIRIAS3296748
description
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Tilborghs, Sofie; Dirks, Ine; Fidon, Lucas; Willems, Siri; Eelbode, Tom; Bertels, Jeroen; Ilsen, Bart; Brys, Arne; Dubbeldam, Adriana; Buls, Nico; Gonidakis, Panagiotis; Sánchez, Sebastián Amador; Snoeckx, Annemiek; Parizel, Paul M; Mey, Johan de; Vandermeulen, Dirk; Vercauteren, Tom; Robben, David; Smeets, Dirk; Maes, Frederik; Vandemeulebroucke, Jef; Suetens, Paul;
2020.
Comparative study of deep learning methods for the automatic segmentation of lung, lesion and lesion type in CT scans of COVID-19 patients..
arXiv; 2020
LIRIAS3296747
description
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presentationSuykens, Jan; Eelbode, Tom; Daenen, Jurgen; Suetens, Paul; Maes, Frederik; Bisschops, Raf; 2020. AUTOMATED POLYP SIZE ESTIMATION WITH DEEP LEARNING REDUCES INTEROBSERVER VARIABILITY. Gastrointestinal Endoscopy; 2020; Vol. 91; iss. 6; pp. AB241 - AB242 Publisher: American Society for Gastrointestinal EndoscopyLIRIAS3397982
description
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De Buck, Stijn; Bertels, Jeroen; Vanbilsen, Chelsey; Dewaele, Tanguy; Ongeval, Chantal Van; Bosmans, Hilde; Vandevenne, Jan; Suetens, Paul;
2020.
Automated breast cancer risk estimation on routine CT thorax scans by deep learning segmentation.
Medical Imaging 2020: Computer-Aided Diagnosis; 2020; Vol. 11314; pp.
Publisher: Society of Photo-optical Instrumentation Engineers
LIRIAS3013831
description
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Bertels, Jeroen; Robben, david; Vandermeulen, dirk; Suetens, paul;
2020.
Optimization with soft Dice can lead to a volumetric bias.
Proceedings BrainLes 2019; 2020; Vol. 11992; pp. 89 - 97
Publisher: Springer-Verlag
LIRIAS2861235
description
Published online -
Robben, David; Boers, Anna MM; Marquering, Henk A; Langezaal, Lucianne LCM; Roos, Yvo BWEM; van Oostenbrugge, Robert J; van Zwam, Wim H; Dippel, Diederik WJ; Majoie, Charles BLM; van der Lugt, Aad; Lemmens, Robin; Suetens, Paul;
2020.
Prediction of final infarct volume from native CT perfusion and treatment parameters using deep learning.
Medical Image Analysis; 2020; Vol. 59; pp.
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LIRIAS2863113
description
CT Perfusion (CTP) imaging has gained importance in the diagnosis of acute stroke. Conventional perfusion analysis performs a deconvolution of the measurements and thresholds the perfusion parameters to determine the tissue status. We pursue a data-driven and deconvolution-free approach, where a deep neural network learns to predict the final infarct volume directly from the native CTP images and metadata such as the time parameters and treatment. This would allow clinicians to simulate various treatments and gain insight into predicted tissue status over time. We demonstrate on a multicenter dataset that our approach is able to predict the final infarct and effectively uses the metadata. An ablation study shows that using the native CTP measurements instead of the deconvolved measurements improves the prediction.
Publisher: Elsevier
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Eelbode, Tom; Suetens, Paul; Bisschops, Raf; Maes, Frederik;
2019.
Semi-supervised video object segmentation as annotation tool for endoscopic video.
https://labels.tue-image.nl/program/; 2019; pp.
Publisher: LABELS workshop
LIRIAS3006715
description
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Sero, Dzemila; Zaidi, Arslan; Li, Jiarui; White, Julie D; Zarzar, Tomas B Gonzalez; Marazita, Mary L; Weinberg, Seth M; Suetens, Paul; Vandermeulen, Dirk; Wagner, Jennifer K; Shriver, Mark D; Claes, Peter;
2019.
Facial recognition from DNA using face-to-DNA classifiers.
Nature Communications; 2019; Vol. 10; iss. 1; pp.
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LIRIAS2812245
description
Facial recognition from DNA refers to the identification or verification of unidentified biological material against facial images with known identity. One approach to establish the identity of unidentified biological material is to predict the face from DNA, and subsequently to match against facial images. However, DNA phenotyping of the human face remains challenging. Here, another proof of concept to biometric authentication is established by using multiple face-to-DNA classifiers, each classifying given faces by a DNA-encoded aspect (sex, genomic background, individual genetic loci), or by a DNA-inferred aspect (BMI, age). Face-to-DNA classifiers on distinct DNA aspects are fused into one matching score for any given face against DNA. In a globally diverse, and subsequently in a homogeneous cohort, we demonstrate preliminary, but substantial true (83%, 80%) over false (17%, 20%) matching in verification mode. Consequences of future efforts include forensic applications, necessitating careful consideration of ethical and legal implications for privacy in genomic databases.
Publisher: Nature Portfolio
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presentationSuetens, Paul; 2019. Role of the MIRC in the AI era.LIRIAS2865615
description
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presentationRobben, David; Christensen, Soren; Boers, Anna M; Marquering, Henk A; Majoie, Charles B; van Oostenbrugge, Robert J; Roos, Yvo B; Dippel, Diederik W; van Zwam, Wim H; van der Lugt, Aad; Lansberg, Maarten G; Albers, Gregory W; Suetens, Paul; Lemmens, Robin; 2019. Deep Learning Based Prediction of Tissue Status From Native CT Perfusion Images.. Stroke; 2019; Vol. 50, suppl. 1; pp. Publisher: American Heart AssociationLIRIAS2836262
description
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Tilborghs, Sofie; Dresselaers, Tom; Claus, Piet; Claessen, Guido; Bogaert, Jan; Maes, Frederik; Suetens, Paul;
2019.
Robust motion correction for cardiac T1 and ECV mapping using a T1 relaxation model approach.
Medical Image Analysis; 2019; Vol. 52; pp. 212 - 227
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LIRIAS2350549
description
T1 and ECV mapping are quantitative methods for myocardial tissue characterization using cardiac MRI, and are highly relevant for the diagnosis of diffuse myocardial diseases. Since the maps are calculated pixel-by-pixel from a set of MRI images with different T1-weighting, it is critical to assure exact spatial correspondence between these images. However, in practice, different sources of motion e.g. cardiac motion, respiratory motion or patient motion, hamper accurate T1 and ECV calculation such that retrospective motion correction is required. We propose a new robust non-rigid registration framework combining a data-driven initialization with a model-based registration approach, which uses a model for T1 relaxation to avoid direct registration of images with highly varying contrast. The registration between native T1 and enhanced T1 to obtain a motion free ECV map is also calculated using information from T1 model-fitting. The method was validated on three datasets recorded with two substantially different acquisition protocols (MOLLI (dataset 1 (n=15) and dataset 2 (n=29)) and STONE (dataset 3 (n = 210))), one in breath-hold condition and one free-breathing. The average Dice coefficient increased from 72.6 ± 12.1% to 82.3 ± 7.4% (P < / 0.05) and mean boundary error decreased from 2.91 ± 1.51mm to 1.62 ± 0.80mm (P < / 0.05) for motion correction in a single T1-weighted image sequence (3 datasets) while average Dice coefficient increased from 63.4 ± 22.5% to 79.2 ± 8.5% (P < / 0.05) and mean boundary error decreased from 3.26 ± 2.64mm to 1.77 ± 0.86mm (P < / 0.05) between native and enhanced sequences (dataset 1 and 2). Overall, the native T1 SD error decreased from 67.32 ± 32.57ms to 58.11 ± 21.59ms (P < / 0.05), enhanced SD error from 30.15 ± 25ms to 22.74 ± 8.94ms (P < / 0.05) and ECV SD error from 10.08 ± 9.59% to 5.42 ± 3.21% (P < / 0.05) (dataset 1 and 2).
Publisher: Elsevier
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Maes, Frederik; Robben, David; Vandermeulen, Dirk; Suetens, Paul;
2019.
The role of medical image computing and machine learning in healthcare.
Artificial intelligence in medical imaging
Opportunities, applications and risks; 2019; pp. 9 - 23
Publisher: Springer
LIRIAS2372585
description
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conferenceRobben, D; Suetens, P; 2019. Perfusion parameter estimation using neural networks and data augmentation. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); 2019; Vol. 11383 LNCS; pp. 439 - 446 Publisher: Springer Verlag keyboard_arrow_downLIRIAS2786496
description
© Springer Nature Switzerland AG 2019. Perfusion imaging plays a crucial role in acute stroke diagnosis and treatment decision making. Current perfusion analysis relies on deconvolution of the measured signals, an operation that is mathematically ill-conditioned and requires strong regularization. We propose a neural network and a data augmentation approach to predict perfusion parameters directly from the native measurements. A comparison on simulated CT Perfusion data shows that the neural network provides better estimations for both CBF and Tmax than a state of the art deconvolution method, and this over a wide range of noise levels. The proposed data augmentation enables to achieve these results with less than 100 datasets.
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conferenceHeylen, Rob; Schramm, Georg; Suetens, Paul; Nuyts, Johan; 2019. 4D CBCT reconstruction with TV regularization on a dynamic software phantom. 2019 IEEE NUCLEAR SCIENCE SYMPOSIUM AND MEDICAL IMAGING CONFERENCE (NSS/MIC); 2019; pp. Publisher: IEEELIRIAS3229945
description
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Bertels, J; Robben, D; Vandermeulen, D; Suetens, P;
2019.
Contra-lateral information CNN for core lesion segmentation based on native CTP in acute stroke.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); 2019; Vol. 11383 LNCS; pp. 263 - 270
Publisher: Springer Verlag
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LIRIAS2786473
description
© Springer Nature Switzerland AG 2019. Stroke is an important neuro-vascular disease, for which distinguishing necrotic from salvageable brain tissue is a useful, albeit challenging task. In light of the Ischemic Stroke Lesion Segmentation challenge (ISLES) of 2018 we propose a deep learning-based method to automatically segment necrotic brain tissue at the time of acute imaging based on CT perfusion (CTP) imaging. The proposed convolutional neural network (CNN) makes a voxelwise segmentation of the core lesion. In order to predict the tissue status in one voxel it processes CTP information from the surrounding spatial context from both this voxel and from a corresponding voxel at the contra-lateral side of the brain. The contra-lateral CTP information is obtained by registering the reflection w.r.t. a sagittal plane through the geometric center. Preprocessed training data was augmented during training and a five-fold cross-validation was used to experiment for the optimal hyperparameters. We used weighted binary cross-entropy and re-calibrated the probabilities upon prediction. The final segmentations were obtained by thresholding the probabilities at 0.50 from the model that performed best w.r.t. the Dice score during training. The proposed method achieves an average validation Dice score of 0.45. Our method slightly underperformed on the ISLES 2018 challenge test dataset with the average Dice score dropping to 0.38.
Published