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Picture for Aad van der Lugt

Aad van der Lugt

for the Alzheimer's Disease Neuroimaging Initiative, on behalf of the Parelsnoer Neurodegenerative Diseases study group

Cross-Cohort Generalizability of Deep and Conventional Machine Learning for MRI-based Diagnosis and Prediction of Alzheimer's Disease


Dec 16, 2020
Esther E. Bron, Stefan Klein, Janne M. Papma, Lize C. Jiskoot, Vikram Venkatraghavan, Jara Linders, Pauline Aalten, Peter Paul De Deyn, Geert Jan Biessels, Jurgen A. H. R. Claassen, Huub A. M. Middelkoop, Marion Smits, Wiro J. Niessen, John C. van Swieten, Wiesje M. van der Flier, Inez H. G. B. Ramakers, Aad van der Lugt

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autoTICI: Automatic Brain Tissue Reperfusion Scoring on 2D DSA Images of Acute Ischemic Stroke Patients


Oct 06, 2020
Ruisheng Su, Sandra A. P. Cornelissen, Matthijs van der Sluijs, Adriaan C. G. M. van Es, Wim H. van Zwam, Diederik W. J. Dippel, Geert Lycklama, Pieter Jan van Doormaal, Wiro J. Niessen, Aad van der Lugt, Theo van Walsum

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* 10 pages; submitted to IEEE TMI 
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Prediction of final infarct volume from native CT perfusion and treatment parameters using deep learning


Dec 06, 2018
David Robben, Anna M. M. Boers, Henk A. Marquering, Lucianne L. C. M. Langezaal, Yvo B. W. E. M. Roos, Robert J. van Oostenbrugge, Wim H. van Zwam, Diederik W. J. Dippel, Charles B. L. M. Majoie, Aad van der Lugt, Robin Lemmens, Paul Suetens

Figure 1 for Prediction of final infarct volume from native CT perfusion and treatment parameters using deep learning
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* Submitted to Medical Image Analysis 
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Segmentation of Intracranial Arterial Calcification with Deeply Supervised Residual Dropout Networks


Jun 04, 2017
Gerda Bortsova, Gijs van Tulder, Florian Dubost, Tingying Peng, Nassir Navab, Aad van der Lugt, Daniel Bos, Marleen de Bruijne

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* Accepted for MICCAI 2017 
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