Ivana Išgum

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Deep Learning for Multi-Task Medical Image Segmentation in Multiple Modalities pdf

Max A. Viergever, Tim Leiner, Jelmer M. Wolterink, 2016, MICCAI.

Deep Learning Techniques for Automatic MRI Cardiac Multi-Structures Segmentation and Diagnosis: Is the Problem Solved?

Xin Yang, Pheng-Ann Heng, Peter M. Full, 2018, IEEE Transactions on Medical Imaging.

Dilated Convolutional Neural Networks for Cardiovascular MR Segmentation in Congenital Heart Disease

Tim Leiner, Ivana Išgum, Jelmer M. Wolterink, 2016, RAMBO+HVSMR@MICCAI.

Deep learning analysis of the myocardium in coronary CT angiography for identification of patients with functionally significant coronary artery stenosis

Max A. Viergever, Nikolas Lessmann, Tim Leiner, 2017, Medical Image Anal..

Towards increased trustworthiness of deep learning segmentation methods on cardiac MRI

Jelmer M. Wolterink, Ivana Išgum, Bob D. de Vos, 2018, Medical Imaging: Image Processing.

A Recurrent CNN for Automatic Detection and Classification of Coronary Artery Plaque and Stenosis in Coronary CT Angiography

Max A. Viergever, Tim Leiner, Jelmer M. Wolterink, 2018, IEEE Transactions on Medical Imaging.

Deep MR to CT Synthesis Using Unpaired Data

Jelmer M. Wolterink, Ivana Išgum, Mark H. F. Savenije, 2017, SASHIMI@MICCAI.

Impact of automatically detected motion artifacts on coronary calcium scoring in chest computed tomography

Nikolas Lessmann, Max A Viergever, Ivana Išgum, 2018, Journal of medical imaging.

ConvNet-Based Localization of Anatomical Structures in 3-D Medical Images

Max A. Viergever, Tim Leiner, Jelmer M. Wolterink, 2017, IEEE Transactions on Medical Imaging.

Iterative fully convolutional neural networks for automatic vertebra segmentation and identification

Nikolas Lessmann, Bram van Ginneken, Pim A. de Jong, 2018, Medical Image Anal..

Deep learning: Generative adversarial networks and adversarial methods

Konstantinos Kamnitsas, Christian Ledig, Jelmer M. Wolterink, 2019 .

Artificial intelligence: improving the efficiency of cardiovascular imaging

Damini Dey, Piotr J Slomka, Ivana Išgum, 2020, Expert review of medical devices.

Generative Adversarial Networks for Noise Reduction in Low-Dose CT

Max A. Viergever, Tim Leiner, Jelmer M. Wolterink, 2017, IEEE Transactions on Medical Imaging.

Relation between clinical risk factors, early cortical changes, and neurodevelopmental outcome in preterm infants

Max A. Viergever, Clara Fischer, Jessica Dubois, 2016, NeuroImage.

Automatic Segmentation of MR Brain Images With a Convolutional Neural Network

Max A. Viergever, Pim Moeskops, Ivana Išgum, 2016, IEEE Transactions on Medical Imaging.

Mutual information for unsupervised deep learning image registration

Jörg Sander, Marius Staring, Bas H. M. van der Velden, 2020, Medical Imaging: Image Processing.

Direct Automatic Coronary Calcium Scoring in Cardiac and Chest CT

Nikolas Lessmann, Tim Leiner, Jelmer M. Wolterink, 2019, IEEE Transactions on Medical Imaging.

Deep Learning-Based Regression and Classification for Automatic Landmark Localization in Medical Images

Paul A. M. Smeets, Tim Leiner, Max A. Viergever, 2020, IEEE Transactions on Medical Imaging.

Coronary artery calcium scoring: Can we do better?

Ivana Išgum, Bob D. de Vos, Tim Leiner, 2020, Medical Imaging: Image Processing.

A pattern recognition approach to automated coronary calcium scoring

Ivana Išgum, B. van Ginneken, M. Prokop, 2004, ICPR 2004.

Automatic Calcium Scoring in Low-Dose Chest CT Using Deep Neural Networks With Dilated Convolutions

Max A. Viergever, Nikolas Lessmann, Bram van Ginneken, 2017, IEEE Transactions on Medical Imaging.

Coronary artery centerline extraction in cardiac CT angiography using a CNN‐based orientation classifier

Max A. Viergever, Tim Leiner, Jelmer M. Wolterink, 2018, Medical Image Anal..

Routine Echocardiography and Artificial Intelligence Solutions

Ivana Išgum, Berto J. Bouma, Mark J Schuuring, 2021, Frontiers in Cardiovascular Medicine.

Medical Imaging 2021: Image Processing

Bennett A. Landman, Ivana Išgum, 2021 .

Graph attention networks for segment labeling in coronary artery trees

Jelmer M. Wolterink, Nils Planken, Ivana Išgum, 2021, Medical Imaging.

Automatic segmentation of the olfactory bulbs in MRI

Paul A. M. Smeets, Sanne Boesveldt, Ivana Išgum, 2021, Medical Imaging.

Untangling the Small Intestine in 3D cine-MRI using Deep Stochastic Tracking

Ivana Išgum, Jaap Stoker, Louis D. van Harten, 2021, MIDL.

Automatic whole-heart segmentation in 4D TAVI treatment planning CT

Jelmer M. Wolterink, Ivana Išgum, Jan Baan, 2021, Medical Imaging.

Quantification of coronary artery calcium in nongated CT to predict cardiovascular events in male lung cancer screening participants: results of the NELSON study.

Tim Leiner, Harry J de Koning, Rozemarijn Vliegenthart, 2015, Journal of cardiovascular computed tomography.

Automatic detection of calcifications in the aorta from CT scans of the abdomen1

Bram van Ginneken, Ivana Išgum, B. Ginneken, 2004 .

Prediction of cognitive and motor outcome of preterm infants based on automatic quantitative descriptors from neonatal MR brain images

Max A. Viergever, Pim Moeskops, Ivana Išgum, 2017, Scientific Reports.

Application of speCtraL computed tomogrAphy to impRove specIficity of cardiac compuTed tomographY (CLARITY study): rationale and design

Tim Leiner, Ivana Išgum, Michiel Voskuil, 2019, BMJ Open.

Automatic quantification of calcifications in the coronary arteries and thoracic aorta on radiotherapy planning CT scans of Western and Asian breast cancer patients.

Nikolas Lessmann, Ivana Išgum, Diederick E Grobbee, 2018, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.

MR-Only Brain Radiation Therapy: Dosimetric Evaluation of Synthetic CTs Generated by a Dilated Convolutional Neural Network.

Jelmer M. Wolterink, Ivana Išgum, Anna M. Dinkla, 2018, International journal of radiation oncology, biology, physics.

Submillisievert coronary calcium quantification using model-based iterative reconstruction: A within-patient analysis.

Tim Leiner, Ivana Išgum, Jelmer M Wolterink, 2016, European journal of radiology.

Erratum to: Automatic determination of cardiovascular risk by CT attenuation correction maps in Rb-82 PET/CT

Piotr J. Slomka, Daniel S. Berman, Damini Dey, 2017, Journal of Nuclear Cardiology.

Development of Cortical Morphology Evaluated with Longitudinal MR Brain Images of Preterm Infants

Max A. Viergever, Pim Moeskops, Ivana Išgum, 2015, PloS one.

Automatic Segmentation of Eight Tissue Classes in Neonatal Brain MRI

Max A. Viergever, Petronella Anbeek, Ivana Išgum, 2013, PloS one.

Deep learning analysis of left ventricular myocardium in CT angiographic intermediate-degree coronary stenosis improves the diagnostic accuracy for identification of functionally significant stenosis

Tim Leiner, Majd Zreik, Max A Viergever, 2018, European Radiology.

Brain Volumes at Term-Equivalent Age in Preterm Infants: Imaging Biomarkers for Neurodevelopmental Outcome through Early School Age.

Petronella Anbeek, Max A Viergever, Ivana Išgum, 2016, The Journal of pediatrics.

Cardiac valve calcifications on low-dose unenhanced ungated chest computed tomography: inter-observer and inter-examination reliability, agreement and variability

Pim A. de Jong, Tim Leiner, Ivana Išgum, 2014, European Radiology.

Automated Assessment of CO-RADS and Chest CT Severity Scores in Patients with Suspected COVID-19 Using Artificial Intelligence

Nikolas Lessmann, Luuk H Boulogne, Bram van Ginneken, 2020, Radiology.

Automated Coronary Artery Calcification Scoring in Non-Gated Chest CT: Agreement and Reliability

Max A. Viergever, Tim Leiner, Matthijs Oudkerk, 2014, PloS one.

Osteoporosis markers on low-dose lung cancer screening chest computed tomography scans predict all-cause mortality

Rozemarijn Vliegenthart, Matthijs Oudkerk, Ivana Išgum, 2014, European Radiology.