no code implementations • 22 Dec 2024 • R. L. M. van Herten, José P. Henriques, R. Nils Planken, Joost Daemen, Eline M. J. Hartman, Jolanda J. Wentzel, Ivana Išgum
The method does not require landmark selection or segmentations as input, while accounting for the presence of IVUS guidewire artifacts.
no code implementations • 29 Oct 2024 • Chenyu Gao, Michael E. Kim, Karthik Ramadass, Praitayini Kanakaraj, Aravind R. Krishnan, Adam M. Saunders, Nancy R. Newlin, Ho Hin Lee, Qi Yang, Warren D. Taylor, Brian D. Boyd, Lori L. Beason-Held, Susan M. Resnick, Lisa L. Barnes, David A. Bennett, Katherine D. Van Schaik, Derek B. Archer, Timothy J. Hohman, Angela L. Jefferson, Ivana Išgum, Daniel Moyer, Yuankai Huo, Kurt G. Schilling, Lianrui Zuo, Shunxing Bao, Nazirah Mohd Khairi, Zhiyuan Li, Christos Davatzikos, Bennett A. Landman
We observe difference between our dMRI-based brain age and T1w MRI-based brain age across stages of neurodegeneration, with dMRI-based brain age being older than T1w MRI-based brain age in participants transitioning from cognitively normal (CN) to mild cognitive impairment (MCI), but younger in participants already diagnosed with Alzheimer's disease (AD).
no code implementations • 18 Sep 2024 • Rudolf L. M. van Herten, Ioannis Lagogiannis, Jelmer M. Wolterink, Steffen Bruns, Eva R. Meulendijks, Damini Dey, Joris R. de Groot, José P. Henriques, R. Nils Planken, Simone Saitta, Ivana Išgum
Deep learning-based medical image segmentation and surface mesh generation typically involve a sequential pipeline from image to segmentation to meshes, often requiring large training datasets while making limited use of prior geometric knowledge.
1 code implementation • 30 Apr 2024 • Dimitrios Karkalousos, Ivana Išgum, Henk A. Marquering, Matthan W. A. Caan
We benchmark 25 DL models on eight publicly available datasets to present distinct applications of ATOMMIC on accelerated MRI reconstruction, image segmentation, quantitative parameter map estimation, and joint accelerated MRI reconstruction and image segmentation utilizing MTL.
no code implementations • 24 Nov 2023 • Laura Alvarez-Florez, Jörg Sander, Mimount Bourfiss, Fleur V. Y. Tjong, Birgitta K. Velthuis, Ivana Išgum
The evaluation of the method is performed on a dataset of cine CMRI scans from 47 ARVC patients and 67 controls.
no code implementations • 6 Nov 2023 • Chenyu Gao, Michael E. Kim, Ho Hin Lee, Qi Yang, Nazirah Mohd Khairi, Praitayini Kanakaraj, Nancy R. Newlin, Derek B. Archer, Angela L. Jefferson, Warren D. Taylor, Brian D. Boyd, Lori L. Beason-Held, Susan M. Resnick, The BIOCARD Study Team, Yuankai Huo, Katherine D. Van Schaik, Kurt G. Schilling, Daniel Moyer, Ivana Išgum, Bennett A. Landman
We find that the ResNet model captures subtler, non-macrostructural features for brain age prediction.
no code implementations • 17 Oct 2023 • Rudolf L. M. van Herten, Nils Hampe, Richard A. P. Takx, Klaas Jan Franssen, Yining Wang, Dominika Suchá, José P. Henriques, Tim Leiner, R. Nils Planken, Ivana Išgum
This requires analysis of the coronary lumen and plaque.
1 code implementation • 3 Oct 2023 • Louis D. van Harten, Jaap Stoker, Ivana Išgum
To improve robustness, we propose a deformable registration method using pairs of cycle-consistent Implicit Neural Representations: each implicit representation is linked to a second implicit representation that estimates the opposite transformation, causing each network to act as a regularizer for its paired opposite.
no code implementations • 9 Aug 2023 • Nils Hampe, Sanne G. M. van Velzen, Jean-Paul Aben, Carlos Collet, Ivana Išgum
Functionally significant coronary artery disease (CAD) is caused by plaque buildup in the coronary arteries, potentially leading to narrowing of the arterial lumen, i. e. coronary stenosis, that significantly obstructs blood flow to the myocardium.
no code implementations • 24 May 2022 • Sanne G. M. van Velzen, Bob D. de Vos, Julia M. H. Noothout, Helena M. Verkooijen, Max A. Viergever, Ivana Išgum
Interscan reproducibility was compared to clinical calcium scoring in radiotherapy treatment planning CTs of 1, 662 patients, each having two scans.
no code implementations • 18 Feb 2022 • Jörg Sander, Bob D. de Vos, Ivana Išgum
Given the unsupervised nature of the method, high-resolution training data is not required and hence, the method can be readily applied in clinical settings.
no code implementations • 24 May 2021 • Sanne G. M. van Velzen, Nils Hampe, Bob D. de Vos, Ivana Išgum
Calcium scoring, a process in which arterial calcifications are detected and quantified in CT, is valuable in estimating the risk of cardiovascular disease events.
1 code implementation • 13 Nov 2020 • Jörg Sander, Bob D. de Vos, Ivana Išgum
The experiments reveal that combining automatic segmentation with simulated manual correction of detected segmentation failures leads to statistically significant performance increase.
no code implementations • 25 Oct 2020 • Jörg Sander, Bob D. de Vos, Ivana Išgum
Instead, lower resolution images are upsampled to higher resolution using conventional interpolation methods.
no code implementations • 1 Oct 2020 • Tycho F. A. van der Ouderaa, Ivana Išgum, Wouter B. Veldhuis, Bob D. de Vos
Deep neural networks are increasingly used for pair-wise image registration.
no code implementations • 10 Aug 2020 • Steffen Bruns, Jelmer M. Wolterink, Richard A. P. Takx, Robbert W. van Hamersvelt, Dominika Suchá, Max A. Viergever, Tim Leiner, Ivana Išgum
Deep learning-based whole-heart segmentation in coronary CT angiography (CCTA) allows the extraction of quantitative imaging measures for cardiovascular risk prediction.
no code implementations • 10 Jul 2020 • Julia M. H. Noothout, Bob D. de Vos, Jelmer M. Wolterink, Elbrich M. Postma, Paul A. M. Smeets, Richard A. P. Takx, Tim Leiner, Max A. Viergever, Ivana Išgum
Global landmark locations are obtained by averaging the predicted displacement vectors, where the contribution of each displacement vector is weighted by the posterior classification probability of the patch that it is pointing from.
no code implementations • 12 Nov 2019 • Louis D. van Harten, Jelmer M. Wolterink, Joost J. C. Verhoeff, Ivana Išgum
We empirically assess how many clinical delineations would be sufficient to train a CNN for the segmentation of OARs and find that increasing the training set size beyond a limited number of images leads to sharply diminishing returns.
no code implementations • 12 Nov 2019 • Louis D. van Harten, Jelmer M. Wolterink, Joost J. C. Verhoeff, Ivana Išgum
We show that this uncertainty measure can be used for two kinds of online quality control.
no code implementations • 10 Nov 2019 • Majd Zreik, Tim Leiner, Nadieh Khalili, Robbert W. van Hamersvelt, Jelmer M. Wolterink, Michiel Voskuil, Max A. Viergever, Ivana Išgum
We combine our previous works for the analysis of the complete coronary artery tree and the LV myocardium: Coronary arteries are encoded by two disjoint convolutional autoencoders (CAEs) and the LV myocardium is characterized by a convolutional neural network (CNN) and a CAE.
no code implementations • 14 Aug 2019 • Jelmer M. Wolterink, Tim Leiner, Ivana Išgum
In this work, we propose to use graph convolutional networks (GCNs) to predict the spatial location of vertices in a tubular surface mesh that segments the coronary artery lumen.
no code implementations • 28 Jun 2019 • Mohamed S. Elmahdy, Jelmer M. Wolterink, Hessam Sokooti, Ivana Išgum, Marius Staring
Joint image registration and segmentation has long been an active area of research in medical imaging.
no code implementations • 11 Jun 2019 • Majd Zreik, Robbert W. van Hamersvelt, Nadieh Khalili, Jelmer M. Wolterink, Michiel Voskuil, Max A. Viergever, Tim Leiner, Ivana Išgum
In patients with obstructive coronary artery disease, the functional significance of a coronary artery stenosis needs to be determined to guide treatment.
no code implementations • 24 Oct 2018 • Jelmer M. Wolterink, Konstantinos Kamnitsas, Christian Ledig, Ivana Išgum
Generative adversarial networks (GANs) and other adversarial methods are based on a game-theoretical perspective on joint optimization of two neural networks as players in a game.
no code implementations • 7 Oct 2018 • Jelmer M. Wolterink, Robbert W. van Hamersvelt, Max A. Viergever, Tim Leiner, Ivana Išgum
Evaluation using 24 test images of the CAT08 challenge showed that extracted centerlines had an average overlap of 93. 7% with 96 manually annotated reference centerlines.
no code implementations • 4 Oct 2018 • Sanne G. M. van Velzen, Majd Zreik, Nikolas Lessmann, Max A. Viergever, Pim A. de Jong, Helena M. Verkooijen, Ivana Išgum
Chest CT scans made in lung cancer screening are suitable for identification of participants at risk of CVD.
no code implementations • 27 Sep 2018 • Steffen Bruns, Jelmer M. Wolterink, Robbert W. van Hamersvelt, Majd Zreik, Tim Leiner, Ivana Išgum
We propose augmentation of the training data with virtual mono-energetic reconstructions from a spectral CT scanner which show different attenuation levels of the contrast agent.
no code implementations • 27 Sep 2018 • Jörg Sander, Bob D. de Vos, Jelmer M. Wolterink, Ivana Išgum
Current state-of-the-art deep learning segmentation methods have not yet made a broad entrance into the clinical setting in spite of high demand for such automatic methods.
no code implementations • 13 Apr 2018 • Julia M. H. Noothout, Bob D. de Vos, Jelmer M. Wolterink, Tim Leiner, Ivana Išgum
Under the assumption that patches close to a landmark can determine the landmark location more precisely than patches farther from it, only those patches that contain the landmark according to classification are used to determine the landmark location.
1 code implementation • 12 Apr 2018 • Nikolas Lessmann, Bram van Ginneken, Pim A. de Jong, Ivana Išgum
Precise segmentation and anatomical identification of the vertebrae provides the basis for automatic analysis of the spine, such as detection of vertebral compression fractures or other abnormalities.
no code implementations • 24 Nov 2017 • Majd Zreik, Nikolas Lessmann, Robbert W. van Hamersvelt, Jelmer M. Wolterink, Michiel Voskuil, Max A. Viergever, Tim Leiner, Ivana Išgum
To identify patients with a functionally significant coronary artery stenosis, analysis is performed in several stages.
no code implementations • 1 Nov 2017 • Nikolas Lessmann, Bram van Ginneken, Majd Zreik, Pim A. de Jong, Bob D. de Vos, Max A. Viergever, Ivana Išgum
On soft filter reconstructions, the method achieved F1 scores of 0. 89, 0. 89, 0. 67, and 0. 55 for coronary artery, thoracic aorta, aortic valve and mitral valve calcifications, respectively.
no code implementations • 20 Apr 2017 • Bob D. de Vos, Floris F. Berendsen, Max A. Viergever, Marius Staring, Ivana Išgum
In this work we propose a deep learning network for deformable image registration (DIRNet).
no code implementations • 19 Apr 2017 • Bob D. de Vos, Jelmer M. Wolterink, Pim A. de Jong, Tim Leiner, Max A. Viergever, Ivana Išgum
We propose a method for automatic localization of one or more anatomical structures in 3D medical images through detection of their presence in 2D image slices using a convolutional neural network (ConvNet).
no code implementations • 12 Apr 2017 • Jelmer M. Wolterink, Tim Leiner, Max A. Viergever, Ivana Išgum
Ten training and ten test CMR scans cropped to an ROI around the heart were provided in the MICCAI 2016 HVSMR challenge.
no code implementations • 11 Apr 2017 • Pim Moeskops, Max A. Viergever, Adriënne M. Mendrik, Linda S. de Vries, Manon J. N. L. Benders, Ivana Išgum
Automatic segmentation in MR brain images is important for quantitative analysis in large-scale studies with images acquired at all ages.
no code implementations • 11 Apr 2017 • Pim Moeskops, Jelmer M. Wolterink, Bas H. M. van der Velden, Kenneth G. A. Gilhuijs, Tim Leiner, Max A. Viergever, Ivana Išgum
The CNN therefore learns to identify the imaging modality, the visualised anatomical structures, and the tissue classes.