5 - Beyond the Patterns - Ivana Isgum - Deep learning for Automatic Detection of Cardiovascular Disease in CT and MR exams/ClipID:24198 previous clip next clip

Recording date 2020-11-18

Via

Free

Language

English

Organisational Unit

Friedrich-Alexander-Universität Erlangen-Nürnberg

Producer

Friedrich-Alexander-Universität Erlangen-Nürnberg

It is a great pleasure to present this invited talk by Ivana Isgum from the University of Amsterdam on her great research in the field of Machine Learning and Medical Imaging:

Title: Deep learning for automatic detection of cardiovascular disease in CT and MR exams 
Prof. Dr. Ivana Išgum, UMC Amsterdam, University of Amsterdam 

Abstract: Deep learning has revolutionized many fields including medical imaging. Routinely acquired cardiac images provide important information for the diagnosis of cardiac disease, and image-guided therapy and intervention. In this presentation, I will show recent development of the AI methods for automatic analysis of cardiac CT and MR exams in my group. Cardiac CT allows visualization of coronary arteries. Hence, I will present our work for a fully automatic analysis of the coronary artery morphology in CT exams. Moreover, to extend the utilization potential of the CT exams, we are developing methods for quantification of cardiac function through analysis of cardiac chambers in 4D CT. Unlike CT, MR is routinely used for the quantification of cardiac function. Therefore, I will present the methods we are developing for the automation of this process. Finally, I will briefly show how we address the interpretability of the automatic decision making, quantification of uncertainty, and other issues related to the implementation of automatic AI methods. 

Biography: Ivana Išgum is University Professor of AI and Medical Imaging at the Amsterdam University Medical Center, University of Amsterdam. In fall 2018 she started as Scientific Lead of the company Quantib-U. Ivana Išgum graduated in Mathematics at the University of Zagreb, Croatia in 1999. She obtained her PhD degree at the Image Sciences Institute in 2007 with a thesis titled ‘Computer-aided detection and quantification of arterial calcifications with CT’. She was a Postdoc at the Laboratory for Clinical and Experimental Image Processing in Leiden University Medical Center, and subsequently Assistant and Associate Professor at UMC Utrecht where she was leading Quantitative Medical Image Analysis (QIA) group at the Image Sciences Institute. In 2019 Ivana was appointed Full Professor and moved with her group to University of Amsterdam. Her group is focusing on the development of algorithms for quantitative analysis of medical images to enable automatic patient risk profiling, diagnosis and prognosis using AI techniques. Ivana Išgum is the recipient of several large grants, has presented extensively in medical image conferences and published in scientific peer-reviewed journals. 

Links to Ivana's Papers:

N. Lessmann et al. IEEE Trans Med Imaging. 2018;37(2):615-625
https://arxiv.org/pdf/1711.00349.pdf

Van Velzen et al. Radiology 2020 Apr;295(1):66-79
https://pubs.rsna.org/doi/10.1148/radiol.2020191621?url_ver=Z39.88-2003&rfr_id=ori:rid:crossref.org&rfr_dat=cr_pub%20%200pubmed

 

Wolterink et al. IEEE Trans Med Imaging. 2017 Dec;36(12):2536-2545
https://ieeexplore.ieee.org/document/7934380

 

Van Velzen et al, SPIE Medical Imaging 2020
https://www.spiedigitallibrary.org/conference-proceedings-of-spie/11313/2549557/Coronary-artery-calcium-scoring-can-we-do-better/10.1117/12.2549557.short

 

Bruns et al. Med Phys 2020, in press

https://arxiv.org/ftp/arxiv/papers/2008/2008.03985.pdf

 

Bruns et al. SPIE Medical Imaging 2021
Not on arXiv (yet)

 

Sander et al. Sci. Rep. 2020; in press
https://arxiv.org/pdf/2011.07025.pdf

 

Sander et al. SPIE Medical Imaging 2021; in press
https://arxiv.org/pdf/2010.13172.pdf

 

 

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Music Reference: Damiano Baldoni - Thinking of You

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