About LKEB

General Information

The Division of Image Processing, in Dutch abbreviated as the LKEB (Laboratorium voor Klinische en Experimentele Beeldverwerking), is a research group within the Department of Radiology, LUMC. The director of LKEB is Prof. Boudewijn P.F. Lelieveldt PhD.

We perform fundamental and applied research in the area of biomedical image processing and analysis. We execute extensive validation studies of the developed techniques, both technically and clinically. We aim to impact the healthcare system by bringing research results close to the clinic, through collaboration with clinicians as well as with industry.

While most of our research focuses on medical imaging, we are also interested in biological and genetic data. We have worked in particular on CT, MRI, IVUS and OCT imaging, in the brain, chest, heart, abdomen, vasculature and bones. In addition, we investigate highly heterogeneous data such as omics, imaging and mass-spectrometry combined, e.g. to produce new insights about structural and functional organization of the brain.

Methodologically we develop artificial intelligence and machine learning technologies such as deep  learning. We combine these with general computer science methods and mathematical modeling. Our work encompasses segmentation, registration, quantification, visualization, radiomics, etc.

To bring our research results close to a clinical end-user we develop high quality software. Our scientific programmers therefore work through a formalized Software Development Process (SDP), often in close collaboration with industry, in particular with Medis medical imaging systems BV. This has led to a number of commercially successful products, and spinoff to a number of large medical imaging vendors. The open source image registration software elastix is also maintained at LKEB. 

Research lines

Contact

For further information contact Prof. Boudewijn P.F. Lelieveldt PhD or through the secretariate (Anna-Carien van der Plas: +31 (0) 71 52 63935).

News

NHS Parliamentary Award voor AI-onderzoeker Rob van der Geest 

NHS Parliamentary Award voor AI-onderzoeker Rob van der Geest 

Op 5 juli 2023 vond in London de uitreiking plaats van de NHS Parliamentary Awards. De eerste prijs in de categorie ‘The Future NHS Award’  werd uitgereikt aan een team van onderzoekers van het Sheffield Teaching Hospital en het LUMC.

Het onderzoeksteam kreeg de prijs vanwege de succesvolle ontwikkeling van een AI-algoritme voor de automatische analyse en kwantificatie van cardiale MRI-beelden en de gerealiseerde implementatie van de ontwikkelde techniek in de klinische workflow. Het AI-algoritme werd ontwikkeld door LUMC-onderzoekers van het Laboratorium voor Klinische en Experimentele Beeldverwerking van de afdeling Radiologie, onder leiding van dr. ir. Rob van der Geest.

Het AI-algoritme maakt het mogelijk om volledig automatisch de cardiale structuren te herkennen in de bewegende MRI-beelden, zodat afwijkingen in de dimensies of de bewegingspatronen van het hart kunnen worden gedetecteerd. Het jury rapport vermeldt onder andere het volgende: “The super-fast analysis could be a ‘game changer’ in future heart disease care – speeding up diagnosis, treatment and care.”

In vervolgonderzoek gaan ze in samenwerking met het universiteitsziekenhuis in Sheffield onderzoeken hoe ze de AI-techniek verder kunnen optimaliseren, zodat deze breder inzetbaar wordt.

Olaf Neve receives price for vestibular schwannoma algorithm

Olaf Neve receives price for vestibular schwannoma algorithm

Drs. Olaf Neve received the price1 for his groundbreaking paper on an innovative deep learning method to automatically segment vestibular schwannomas (brughoektumor in Dutch) on MRI.

Vestibular schwannomas are rare intracranial tumors. Some remain stable and do not require intervention, others progress and do require surgery or radiotherapy. The algorithm gives fast and accurate automated measurements and can detect tumor growth, which has important implications for the decision to treat.

The work is a close collaboration between the Departments of Otorhinolaryngology and Radiology of Leiden University Medical Center, with deep learning contributions from Yunjie Chen and dr. Qian Tao (LKEB), and clinical input from dr. Berit Verbist (Radiology) and dr. Erik Hensen (Otorhinolaryngology). In an observer study2 the team showed that the deep learning tool was similar to human delineation in 85% – 92% of cases from a very diverse cohort (from multiple hospitals, scanned on various MR machines), indicating that the tool is robust enough for clinical deployment. With help of the LUMC CAIRELab, clinical implementation is indeed currently pursued.

Example deep learning delineations on challenging cases with cystic components.

The award was created by hearing aid company “Beter Horen” for publications by young Otorhinolaryngology physicians, as a recognition for their work.

1 https://www.beterhoren.nl/wetenschapsprijs

2 https://doi.org/10.1148/ryai.210300

PhD student in AI research Radiology receives Best Abstract Award

PhD student in AI research Radiology receives Best Abstract Award

Yanli Li, PhD student at the division of image processing (LKEB) of the Radiology department, received in Milan during the international rheumatology congress, EULAR, the  Best Abstract Award for Clinical Science. In collaboration with the Rheumatology department, he studied whether his deep learning models would be able to predict the early development of rheumatoid arthritis from MRI scans of the hands, wrists and feet of patients with clinically suspect arthralgia.

https://www.lumc.nl/over-het-lumc/actueel/2023/juni/prijzen-subsidies-en-benoemingen/

Can a computer replace human interpretation?

Can a computer replace human interpretation?

EULAR – the European Alliance of Associations for Rheumatology – begins its 2023 annual congress on 31st May in Milan, Italy. Among the data being presented, researchers from the Netherlands ask whether artificial intelligence interpretation of MRI images can provide more accurate predictions than visual scoring in early rheumatoid arthritis (RA).

Go to Press Release

Looking for a challenge in medical imaging?

Looking for a challenge in medical imaging?

We have two PhD positions available for our new ICAI lab AI4MRI, that aims to accelerate and improve MR imaging.

PhD Candidate in AI-based Reconstruction of Cardiac MRI

PhD Position on the management of artificial intelligence (AI) in healthcare & policy

Artificial intelligence project om MRI-scans te versnellen ontvangt 2 miljoen euro

Artificial intelligence project om MRI-scans te versnellen ontvangt 2 miljoen euro

Het Leids Universitair Medisch Centrum (LUMC), de Universiteit Leiden en Philips ontvangen samen ruim 2 miljoen euro van NWO om een artificial intelligence (AI) laboratorium op te zetten. Het doel van dit lab is om MRI-scans te versnellen en te verbeteren met AI. Fijn voor de patiënt, én het helpt MRI toegankelijker te maken.

Voor meer informatie: LUMC, Universiteit Leiden, NWO

Job Opportunity: PhD Candidate Machine Learning for Brain Tumor Management

Job Opportunity: PhD Candidate Machine Learning for Brain Tumor Management

Vestibular schwannomas (VS) are rare intracranial tumors that are (typically) benign, but may cause invalidating symptoms such as hearing loss, balance disturbance or even intracranial hypertension and brainstem compression in advanced cases. Typically, patients are monitored closely with periodic MR imaging, and only in case of tumor progression, treatment is opted for. Computational tools based on Machine Learning may allow prediction of tumor progression in an early stage, when less invasive treatment strategies are still an option. To support this clinical decision, in this PhD project you will develop Artificial Intelligence and Machine Learning methods to predict a patient-specific risk of progression, growth rate, and optimal monitoring frequency. These predictions are predominantly based on longitudinal MR imaging, and can be augmented with relevant clinical information, such as audiometry, symptom-based tests, age and sex. You will likely use and develop multi-task learning strategies, convolutional neural networks and transformers, and spatio-temporal image registration methods. Since LUMC is a national referral center for VS, a very large cohort of multi-center patient data is available, which will be extended with data shared by Erasmus MC. We recently developed a volumetric segmentation model for VS, showing excellent performance on a diverse dataset, which can be used during the project. The methods you develop will be integrated in the clinical workflow by a clinical fellow, to establish their added clinical value, already during the course of your PhD.

For more info see: https://www.lumc.nl/en/over-het-lumc/werken-bij/vacancies/b.23.pv.jh.10-phd-candidate-machine-learning-for-brain-tumor-management/

Grant for development of AI-based tumor growth prediction models

Grant for development of AI-based tumor growth prediction models

A team of the Departments of Radiology and  Otorhinolaryngology and Head & Neck Surgery received a grant from the Hanarth Fonds to develop and clinically implement machine learning models for growth estimation of vestibular schwannomas.

Vestibular schwannomas (VS) are rare intracranial tumors that are (typically) benign, but may cause invalidating symptoms such as hearing loss, balance disturbance or even intracranial hypertension and brainstem compression in advanced cases. Typically, patients are monitored closely with periodic MR imaging, and only in case of tumor progression, treatment is opted for. Computational tools based on Machine Learning may allow prediction of tumor progression in an early stage, when less invasive treatment strategies are still an option.

The Hanarth Fonds awarded € 400 000 to the MLSCHWAN project, to enable the group of prof. Marius Staring to develop the computational tools. Dr. Erik Hensen, dr. Berit Verbist and dr. Willem Grootjans will then translate these tools to the clinic, and execute retrospective and prospective studies to determine their added value, comparing a workflow with and without AI assistance. In collaboration with Erasmus MC the world’s largest patient cohort is available for training the models, since both centers are national referral centers for VS. The LUMC CAIRELAB supports the translational AI aspects of the project.

The Hanarth Fonds awarded this year in total 13 projects on machine learning for rare tumors, for a total of € 4.4 million: https://www.hanarthfonds.nl/nl/gehonoreerde-aanvragen/2022-call.

LUMC gaat meebouwen aan meest gedetailleerde atlas van menselijk brein tot nu toe

LUMC gaat meebouwen aan meest gedetailleerde atlas van menselijk brein tot nu toe

Een toonaangevend team van internationale hersenonderzoekers wil de ongeveer 200 miljard cellen in het menselijk brein in kaart brengen volgens hun type en functie. Een project waar zo’n 110 miljoen dollar mee gemoeid is. Het Leids Universitair Medisch Centrum (LUMC) ontwikkelt technieken voor datavisualisatie om de enorme bak aan complexe data die hieruit voorkomt inzichtelijker te maken. De verwachting is dat deze atlas het onderzoek naar de oorzaken en behandelingen van hersenziektes in een stroomversnelling brengt.

Lees het hele bericht op lumc.nl

New ‘Machine Learning’ Professor will focus on Medical Imaging

New ‘Machine Learning’ Professor will focus on Medical Imaging

Marius Staring has been appointed Professor of ‘Machine Learning’ for Medical Imaging as of 1 April, 2022. Staring hopes to contribute to faster and better image processing techniques with relatively new algorithms, such as deep learning [lumc.nl]

SURF Research Support Champions 2022

SURF Research Support Champions 2022

Congratulations to Michèle Huijberts for being one of the SURF Research Support Champions 2022. Michèle goes above and beyond to support all the digital needs in our group.” . https://www.surf.nl/en/news/surf-research-support-champions-2022-sarah-coombs-serkan-girgin-michele-huijberts

PhD defence: Mohamed S. Elmahdy

PhD defence: Mohamed S. Elmahdy

Mohamed S. Elmahdy

Date:             Tuesday March 15, 2022
Time:             15:00
Venue:           online|Academy Building, Rapenburg 73, Leiden
Thesis title:   Deep learning for online adaptive radiotherapy

PhD defence: Antonios Somarakis

PhD defence: Antonios Somarakis

Antonios Somarakis

Date:             Thursday 20 january 2022         
Time:             11.15      
Venue:           online|Academy Building, Rapenburg 73, Leiden
Thesis title:   Visual Analytics for Spatially Resolved OMICS Data at Single Cell Resolution: Methods & Application

PhD defence: Hessam Sokooti Oskooyi

PhD defence: Hessam Sokooti Oskooyi

Hessam Sokooti Oskooyi

Date:             Thursday 25 November 2021         
Time:            16:15 – 17:00 
Venue:           online|Academy Building, Rapenburg 73, Leiden
Thesis title:  Supervised learning for medical image registration

Zwaartekracht-consortium draagt bij aan het in kaart brengen van de motorische cortex van de hersenen

Zwaartekracht-consortium draagt bij aan het in kaart brengen van de motorische cortex van de hersenen

Twee co-leiders van het Brainscapes-project van NWO Zwaartekracht hebben succesvol bijgedragen aan het in kaart brengen van verschillende celtypen van corticale gebieden van het brein, waaronder de motor cortex. Nature publiceerde een speciale editie over de resultaten van het internationale BRAIN initiative Cell Census Network. De resultaten laten zien hoe genetici, bioinformatici en neurowetenschappers samenwerken om betere behandelingen te ontwikkelen voor hersenziekten.

[nwo][brainscapes]

CZI funds further development of Elastix

CZI funds further development of Elastix

The Chan Zuckerberg Initiative (CZI) has funded several open source software projects in the field of biomedicine and Elastix is one of the recipients.

https://www.lumc.nl/over-het-lumc/nieuws/2021/september/een-zomer-vol-prijzen-en-subsidies/

https://github.com/SuperElastix/elastix/

Job Opportunity: PhD Candidate AI-based MR Reconstruction Methods Radiology

Job Opportunity: PhD Candidate AI-based MR Reconstruction Methods Radiology

The Radiology department, in collaboration with Philips Electronics, is looking for an ambitious PhD candidate with affinity for MRI techniques and artificial intelligence. Is this you? Then don’t hesitate to apply!

PhD-kandidaat-AI-gebaseerde-MR-reconstructie-Radiologie

Successful collaboration between LUMC and LIACS on AI for radiotherapy

Successful collaboration between LUMC and LIACS on AI for radiotherapy

Daily-adapted radiotherapy can help to more precisely target radiation dose to tumors compared to the current clinical practice, while avoiding radiosensitive organs-at-risk in the surrounding area. A main obstacle however is that new treatment plans need to be created every day, which is a manual and time-consuming process. A team from LUMC and LIACS recently created AI technology that can do this fully automatically with promising accuracy and in real-time.

https://www.universiteitleiden.nl/en/sails/news/2021/lumc–liacs-collaboration-success

Elastix/Napari plugin

Elastix/Napari plugin

There is now a @napari_imaging plugin for fast image registration based on ITKElastix, the python wrapper for Elastix.

Job opportunity: Postdoc Computational Oncology

Job opportunity: Postdoc Computational Oncology

We are looking for a postdoc for the division of Image Processing (LKEB), in collaboration with the department of Pathology (Molecular Tumour Genetics, research programs Bone and Soft Tissue Tumours and Immunogenomics). Do you want to contribute to groundbreaking cancer research, and are you experienced in computational approaches? Then you might be the postdoc we are looking for.

PhD defence: Paulien Stegehuis

PhD defence: Paulien Stegehuis

Paulien Stegehuis

Date:              Tuesday, 20 April 2021        
Time:             11:15 AM     
Venue:           online|Academy Building, Rapenburg 73, Leiden
Thesis title:   The use of optical techniques to guide cancer diagnosis and radical surgical resections

PhD defence: Arlin Keo

PhD defence: Arlin Keo

Arlin Keo

Date:             Thursday, 3 December 2020
Time:             11:15 AM
Venue:           online (https://www.universiteitleiden.nl/wetenschappers/livestream-promotie)
Thesis title:  Analyzing Spatial Transcriptomics and NeuroImaging data in Neurodegenerative Diseases

PhD defence: Zhiwei Zhai

PhD defence: Zhiwei Zhai


Zhiwei Zhai

Date:             Tuesday, 10 March 2020
Time:             11:15
Venue:           Academy Building, Rapenburg 73, Leiden
Thesis title:  Automatic Quantitative Analysis of Pulmonary Vessels in CT: Methods and Applications

PhD defence: Qing Cao

PhD defence: Qing Cao


Qing Cao

Date:             Tuesday, 21 January 2020
Time:             10:00 AM
Venue:           Academy Building, Rapenburg 73, Leiden
Thesis title:  Automated Analysis Approaches for Coronary CT Angiography

Challenge won

Challenge won


14 December 2019, a team of LKEB, Gorter Center and Philips won 2 tracks in the highly prestigious fastMRI reconstruction challenge organized by Facebook AI, using a deep learning method.

Nature Immunology

Nature Immunology


19 Februari 2019, Team Cytosplore (LUMC + TU Delft) publication highlighted on the March cover of Nature Immunology: Memory CD4+ T cells are generated in the human fetal intestine see this press release

PhD defence: Nicola Pezzotti

PhD defence: Nicola Pezzotti


Nicola Pezzotti

Date:             Monday, 8th April, 2019
Time:            10:00 h
Venue:          Senaatszaal of the Auditorium, Mekelweg 5, Delft
Thesis title: Dimensionality-Reduction Algorithms for Progressive Visual Analytics

PhD defence: Evgeni Aizenberg

PhD defence: Evgeni Aizenberg


Evgeni Aizenberg

Date:             Tuesday, 14 March 2019
Time:            13:45 h
Venue:          Academy Building, Rapenburg 73, Leiden
Thesis title: Computer-aided techniques for assessment of MRI-detected inflammation for early identification of inflammatory arthritis

Nvidia

Nvidia


24 January 2019, Our new, dedicated deep learning hardware arrived: a NVIDIA DGX station for state-of-the-art medical deep learning research @ LKEB

PhD defence: Yingguang Li

PhD defence: Yingguang Li


Yingguang Li

Date:             Tuesday, 9 October 2018
Time:            11:15h
Venue:          Academy Building, Rapenburg 73, Leiden
Thesis title: Fusion of X-ray angiography and Optical Coherence Tomography for coronary flow simulation

PhD defence: Shengan Liu

PhD defence: Shengan Liu


Shengan Liu

Date:             Tuesday 4 September 2018
Time:            16:15h
Venue:          Academy Building, Rapenburg 73, Leiden
Thesis title: Optical coherence Tomography for Coronary Artery Disease: Analysis and Applications

PhD defence: Yuchuan Qiao

PhD defence: Yuchuan Qiao


Yuchuan Qiao

Date:             Wednesday, November 1, 2017
Time:            10:00 h
Venue:          Academy Building, Rapenburg 73, Leiden
Thesis title: Fast Optimization Methods for Image Registration in Adaptive Radiation Therapy

People


  • Chairs
  • Emeritus chair
  • Faculty
  • PhD candidates
  • Scientific staff
  • Secretariat
  • Technical staff
Donghang Lyu

Donghang Lyu

I am Donghang Lyu. My research is about using AI methods to accelerate 4D cardiac MRI by integrating some info like motion to improve image quality. I got my bachelor's degr...
Detail
Vincent van der Sluis

Vincent van der Sluis

...
Detail
Tahereh Hassanzadeh

Tahereh Hassanzadeh

Introduction Tahereh Hassanzadeh obtained a Master degree in Artificial Intelligence (AI) from Azad University–Qazvin branch, Iran (2012) and a Master of Research degree in ...
Detail
Manuel Goldkuhle

Manuel Goldkuhle

Introduction My PhD research at the LKEB focuses on the prediction of vestibular schwannoma tumor growth from multi-modal patient data using machine learning techniques. Based o...
Detail
Yanli Li

Yanli Li

Introduction Yanli Li is a PhD candidate at LKEB investigating deep learning methods for medical imaging under the supervision of Dr. Berend Stoel and Prof. Marius Staring. ...
Detail
Ruochen Gao

Ruochen Gao

Introduction Ruochen Gao is a PhD candidate at LKEB investigating robust deep learning methods for medical imaging under the supervision of Prof Marius Staring. Prior to tha...
Detail
Soumyadeep Basu

Soumyadeep Basu

Introduction Soumyadeep is a Ph.D. student working on the development of data visualization methods for heterogeneous brain informatic resources. He completed his B.Tech. in Ele...
Detail
Chinmay Rao

Chinmay Rao

Introduction Chinmay is a PhD candidate at LKEB investigating MRI reconstruction methods for accelerating multicontrast imaging. MR acquisition is inherently slow due to physica...
Detail
Boudewijn Lelieveldt

Boudewijn Lelieveldt

Introduction Boudewijn P.F. Lelieveldt is professor of Biomedical Imaging at the Department of Radiology, Leiden University Medical Center, Leiden, the Netherlands, where he is ...
Detail
Silvia Pintea

Silvia Pintea

...
Detail
Xiaotong Zhang

Xiaotong Zhang

Introduction Xiaotong Zhang obtained her Master degree of Biomedical Engineering in January 2020, at the Northeastern University, China. Her master thesis is ‘Research on Quanti...
Detail
Laurens Beljaards

Laurens Beljaards

Introduction Laurens obtained his master's and bachelor's degrees in computer science at LIACS, Leiden University. After writing his master's thesis at the Division of Image Pro...
Detail
Chang Li

Chang Li

Introduction Chang Li received her MSc degree in Mechanical Engineering at Leibniz University Hannover, Germany and her BSc degree in Aerospace Engineering at Beijing Institute ...
Detail
Viktor van der Valk

Viktor van der Valk

Introduction I’m a scientific (Python) programmer in the LKEB group at the LUMC. In the LKEB group I’m working on the elastix image registration toolbox. In particular on elasti...
Detail
Yunjie Chen

Yunjie Chen

Introduction I obtained Master degree in Biomedical Engineering from Shanghai Jiao Tong University, China in 2018. Since November 2020, I pursued PhD study focused on Quantitati...
Detail
Test

Test

#primary { width: 90%; padding-left: 200px; } Introduction   Area(s) of interest   External activities   Researcher profiles   Key publications   C...
Detail
Prerak Mody

Prerak Mody

Introduction Prerak Mody is pursuing his PhD on "Human-AI Interaction for Contour Propagation in an Adaptive Radiotherapy Context". Using machine learning methods, he is explori...
Detail
Li-Hsin Cheng

Li-Hsin Cheng

Introduction Li-Hsin Cheng received her Master's degree in Electrical Engineering from National Tsing Hua University, Taiwan. Since March 2020, she joined the Division of Image ...
Detail
Anna-Carien

Anna-Carien

...
Detail
Ahmed Mahfouz

Ahmed Mahfouz

I am a postdoctoral researcher in the Imaging Genetics section at the Radiology Department of Leiden University Medical Center. I am also a member of the Delft Bioinformatics Lab...
Detail
Hans Reiber

Hans Reiber

Introduction Johan H.C. Reiber received his M.Sc. EE-degree from the Delft  University of Technology in 1971 and his M.Sc and Ph.D. from Stanford  University, USA in 1975 and 19...
Detail
Denis Shamonin

Denis Shamonin

Introduction Denis P. Shamonin received master's degree in Applied Mathematics, St. Petersburg State Marine Technical University,Dept. of Applied Mathematics in 2002. After grad...
Detail
Patrick de Koning

Patrick de Koning

Introduction Patrick de Koning obtained his Master of Science in Electrical Engineering in 1998 at the Delft University of Technology, Delft, The Netherlands. His Master’s thesi...
Detail
Qian Tao

Qian Tao

Introduction Qian Tao received her BSc degree (with honors) in Electrical Engineering from Fudan University, Shanghai, China, in 2001. She received her MSc degree (with honors) ...
Detail
Els Bakker

Els Bakker

Introduction M. Els Bakker studied Biology at the University of Leiden. She graduated in August 1983. From 1987-1991 she worked as PhD student at National Herbarium in Leiden ...
Detail
Mohamed Hassan

Mohamed Hassan

Introduction Mohamed Kilany Hassan graduated from Biomedical Engineering department in Cairo University in 2008. He worked as a Research Assistance at Nile University for two ye...
Detail
Jingnan Jia

Jingnan Jia

Introduction Jingnan Jia received his Bachelor degree of Applied Physics in July 2015, at Taiyuan University of Technology, and Master degree of Electromagnetic Fields and Waves...
Detail
Kirsten Koolstra

Kirsten Koolstra

Introduction Kirsten studied Applied Mathematics at the Delft University of Technology (2010-2015) and worked during her master thesis on the modeling of electromagnetic fields ...
Detail
Alexander Vieth

Alexander Vieth

...
Detail
Irene Hernández Girón

Irene Hernández Girón

Introduction Dr. Irene Hernandez-Giron is a medical imaging researcher at the Division of Image Processing, working on advanced methods for image quality assessment combining 3D...
Detail
Yichao Li

Yichao Li

...
Detail
Marius Staring

Marius Staring

Introduction Marius Staring is a professor of Machine Learning for Medical Imaging, and vice director of the Division of Image Processing (Dutch abbreviation LKEB), at the L...
Detail
Rob van der Geest

Rob van der Geest

Introduction Rob J. van der Geest received his MSc degree in Electrical engineering from the Delft University of Technology in 1992. In his masters thesis he developed an automa...
Detail
Oleh Dzyubachyk

Oleh Dzyubachyk

Introduction Oleh Dzyubachyk received a M.Sc. degree (cum laude) in Mathematics from Ivan Franko National University (Lviv, Ukraine) in 1998. In 2011 he obtained his PhD degree ...
Detail
Berend Stoel

Berend Stoel

Introduction Dr. Berend C. Stoel is Associate Professor at the Division of Image Processing, heading the section of Pulmonary, Musculoskeletal & Ophthalmologic Imaging (...
Detail
Jouke Dijkstra

Jouke Dijkstra

Introduction Dr. Dijkstra is associate Professor at LUMC-LKEB, where he is the leader of the "vascular and molecular imaging" section. He has been working at the division of ima...
Detail
Alexander Broersen

Alexander Broersen

  Introduction Alexander Broersen is a scientific researcher in the section ‘vascular & molecular imaging’ in the division of image processing. He received his M.Sc....
Detail
Niels Dekker

Niels Dekker

Introduction Niels Dekker is scientific programmer in the section Vascular and Molecular Imaging of the LKEB. In 1992, Niels obtained his Master of Science on Computer Scienc...
Detail
Baldur van Lew

Baldur van Lew

Introduction Baldur van Lew received a B.A. in Natural Sciences (Physics) from King's College Cambridge in 1981. While working for the Burroughs Corporation he completed a postg...
Detail
Michèle Huijberts

Michèle Huijberts

Introduction Michèle Huijberts obtained his Master of Medical Electrotechnical Engineering in 1992 at the Technical University Eindhoven, The Netherlands. After graduation he st...
Detail
Leo Wolf

Leo Wolf

Introduction Leo Wolf was originally educated as an electrical engineer on college level (Dutch: HTS). Later he studied computer science at LIACS, Leiden University. He worked a...
Detail
Jeroen Eggermont

Jeroen Eggermont

Introduction Jeroen Eggermont is a scientific researcher in the section ‘vascular & molecular imaging’ in the division of image processing. He received his M.Sc. degree in c...
Detail
Thomas Kroes

Thomas Kroes

Introduction Thomas Kroes works as postdoctoral researcher at the Division of Image Processing at the Leiden University Medical Center. During his PhD, he worked in the field of...
Detail
Xiaowu Sun

Xiaowu Sun

Introduction Xiaowu Sun earned his Master degree of Software Engineering in June 2018, at the Capital Normal University, China. His master thesis is “Protein complexes predictio...
Detail
Arlin Keo

Arlin Keo

...
Detail
Hessam Sokooti

Hessam Sokooti

Introduction Hessam Sokooti Oskooyi earned a bachelor’s degree in Electrical Engineering at University of Tehran in September 2011. He finished his Master thesis “Fluorescein an...
Detail
Antonios Somarakis

Antonios Somarakis

Introduction Antonios Somarakis received his Diploma (MSc equivalent) in Electrical & Computer engineering from National Technical University of Athens (Athens,Greece). ...
Detail
Sahar Yousefi

Sahar Yousefi

Introduction Sahar works at GorterCenter. Her current research focus is perfusion MRI reconstruction using deep learning. She has one year of experience of research vis...
Detail
Mohamed S. Elmahdy

Mohamed S. Elmahdy

Introduction Mohamed Elbially Elmahdy earned his a bachelor’s degree in Biomedical Engineering at Cairo University, Egypt in June 2013. He worked as a Teaching and Research ...
Detail
Zhiwei Zhai

Zhiwei Zhai

Introduction Zhiwei Zhai received his Bachelor degree of Information and Computing Science in July 2012, at Harbin Institute of Technology (Weihai), and Master degree of Com...
Detail