Deep learning in medical image analysis (DLMedIA)

Imaging is a cornerstone of medicine. The number and volume of radiology exams is growing rapidly thereby tremendously increasing the workload of radiologists. Deep learning methods can potentially extract more information from images, more reliably, more accurately, and most notably fully automatically.
The goal of the DLMedIA programme is two fold: 1) Develop a technological software platform for application development and; 2) build specific solutions for personalized and precision medicine. Through four different projects and in collaboration with four research centers and two industrial partners (Philips Healthcare, Pie Medical Imaging) , in our group we focus on early detection and prevention cardiovascular disease.

Research projects

High dimensional data
Goal: To develop deep learning techniques for quantitative analysis of 4- and 5-dimensional medical images.

Deep generative models
Goal: To incorporate expert knowledge in the form of generative models in deep learning in order to learn more efficiently from less data.
Application: Blood vessel geometry synthesis using generative adversarial networks

Deep transfer learning
Goal: To develop deep transfer learning techniques to effectively analyze heterogeneous medical imaging data with variation in scanners, scan protocols, and patient populations.

Dynamic deep learning
Goal: To develop dynamic learning strategies for deep learning systems in clinical environments.

Research partners

Prof. dr. Bram van Ginneken, Diagnostic Image Analysis Group, Radboud University Medical Center, Nijmegen
Dr. Clarisa Sanchez, Diagnostic Image Analysis Group, Radboud University Medical Center, Nijmegen
Prof. dr. Max Welling, Amsterdam Machine Learning Lab, Department of Science University of Amsterdam (UvA)
Dr. Marleen de Bruijne, Biomedical Imaging Group Rotterdam, Erasmus Medical Center Rotterdam
Prof. dr. Josien Pluim Medical Image Analysis, Eindhoven University of Technology

Researchers


Related publications

Journal Articles

1.

J. Sander, B.D. de Vos, I. Išgum

Automatic segmentation with detection of local segmentation failures in cardiac MRI Journal Article

Scientific Reports, 10 (21769 ), 2020.

Abstract | Links | BibTeX

2.

S. Bruns, J.M. Wolterink, R.A.P. Takx, R.W. van Hamersvelt, D. Suchá, M.A. Viergever, T. Leiner, I. Išgum

Deep learning from dual-energy information for whole-heart segmentation in dual-energy and single-energy non-contrast-enhanced cardiac CT Journal Article

Medical Physics, 47 (10), pp. 5048-5060, 2020.

Abstract | Links | BibTeX

3.

J.M.H. Noothout, B.D. de Vos, J.M. Wolterink, E.M. Postma, P.A.M. Smeets, R.A.P. Takx, T. Leiner, M.A. Viergever, I. Išgum

Deep learning-based regression and classification for automatic landmark localization in medical images Journal Article

IEEE Transactions on Medical Imaging, 39 (12), pp. 4011-4022, 2020, ISSN: 1558-254X.

Abstract | Links | BibTeX

4.

R.W. van Hamersvelt, I. Išgum, P.A. de Jong, M.J. Cramer, G.E. Leenders, M.J. Willemink, M. Voskuil, T. Leiner

Application of speCtraL computed tomogrAphy to impRove specIficity of cardiac compuTed tomographY (CLARITY study): Rationale and Design Journal Article

BMJ Open, 9 (3), pp. e025793, 2019.

Abstract | Links | BibTeX

Inproceedings

1.

J.M.H. Noothout, E.M. Postma, S. Boesveldt, B.D. de Vos, P.A.M. Smeets, I. Išgum

Automatic segmentation of the olfactory bulbs in MRI Inproceedings

In: SPIE Medical Imaging, pp. 115961J, 2021.

Abstract | Links | BibTeX

2.

J. Sander, B.D. de Vos, I. Išgum

Unsupervised super-resolution: creating high-resolution medical images from low-resolution anisotropic examples Inproceedings

In: SPIE Medical Imaging, pp. 115960E, 2021.

Abstract | Links | BibTeX

3.

S. Bruns, J.M. Wolterink, T.P.W. van den Boogert, J.P. Henriques, J. Baan, R.N. Planken, I. Išgum

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

In: SPIE Medical Imaging, pp. 115960B, 2021.

Abstract | Links | BibTeX

4.

B.D. de Vos, B.H.M. van der Velden, J. Sander, K.G.A. Gilhuijs, M. Staring, I. Išgum

Mutual information for unsupervised deep learning image registration Inproceedings

In: SPIE Medical Imaging, pp. 113130R, 2020.

Abstract | Links | BibTeX

5.

J.M. Wolterink, T. Leiner, I. Išgum

Graph convolutional networks for coronary artery segmentation in cardiac CT angiography Inproceedings

In: Graph Learning in Medical Imaging (GLMI 2019), Lecture Notes in Computer Science, 2019.

Links | BibTeX

6.

S. Bruns, J.M. Wolterink, R.W. van Hamersvelt, T. Leiner, I. Išgum

CNN-based segmentation of the cardiac chambers and great vessels in non-contrast-enhanced cardiac CT Inproceedings

In: Medical Imaging with Deep Learning (MIDL 2019), 2019.

Abstract | Links | BibTeX

7.

S. Bruns, J.M. Wolterink, R.W. van Hamersvelt, M. Zreik, T. Leiner, I. Išgum

Improving myocardium segmentation in cardiac CT angiography using spectral information Inproceedings

In: SPIE Medical Imaging, pp. 109492M, 2019.

Abstract | Links | BibTeX

8.

J. Sander, B.D. de Vos, J.M. Wolterink, I. Išgum

Towards increased trustworthiness of deep learning segmentation methods on cardiac MRI Inproceedings

In: SPIE Medical Imaging, pp. 1094919, 2019.

Abstract | Links | BibTeX

9.

J.M. Wolterink, T. Leiner, I. Išgum

Blood vessel geometry synthesis using generative adversarial networks Inproceedings

In: Medical Imaging with Deep Learning (MIDL 2018), 2018.

Abstract | Links | BibTeX

10.

J.M.H. Noothout, B.D. de Vos, J.M. Wolterink, T. Leiner, I. Išgum

CNN-based Landmark Detection in Cardiac CTA Scans Inproceedings

In: Medical Imaging with Deep Learning (MIDL 2018), 2018.

Abstract | Links | BibTeX

Abstracts

1.

J.M.H. Noothout, B.D. de Vos, J.M. Wolterink, R.A.P. Takx, T. Leiner, I. Išgum

Deep learning for automatic landmark localization in CTA for transcatheter aortic valve implantation Abstract

In: Radiological Society of North America, 105th Annual Meeting, 2019.

Abstract | Links | BibTeX

PhD Theses

1.

R.W. van Hamersvelt

New dimensions in cardiovascular CT PhD Thesis

Utrecht University, The Netherlands, 2019, ISBN: 978-90-393-7092-6.

BibTeX