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Your search keyword '"Jäger, H Rolf"' showing total 8 results
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8 results on '"Jäger, H Rolf"'

1. Unsupervised 3D out-of-distribution detection with latent diffusion models

Author

Graham, Mark S., Pinaya, Walter Hugo Lopez, Wright, Paul, Tudosiu, Petru-Daniel, Mah, Yee H., Teo, James T., Jäger, H. Rolf, Werring, David, Nachev, Parashkev, Ourselin, Sebastien, Cardoso, M. Jorge, Graham, Mark S., Pinaya, Walter Hugo Lopez, Wright, Paul, Tudosiu, Petru-Daniel, Mah, Yee H., Teo, James T., Jäger, H. Rolf, Werring, David, Nachev, Parashkev, Ourselin, Sebastien, and Cardoso, M. Jorge

Abstract

Methods for out-of-distribution (OOD) detection that scale to 3D data are crucial components of any real-world clinical deep learning system. Classic denoising diffusion probabilistic models (DDPMs) have been recently proposed as a robust way to perform reconstruction-based OOD detection on 2D datasets, but do not trivially scale to 3D data. In this work, we propose to use Latent Diffusion Models (LDMs), which enable the scaling of DDPMs to high-resolution 3D medical data. We validate the proposed approach on near- and far-OOD datasets and compare it to a recently proposed, 3D-enabled approach using Latent Transformer Models (LTMs). Not only does the proposed LDM-based approach achieve statistically significant better performance, it also shows less sensitivity to the underlying latent representation, more favourable memory scaling, and produces better spatial anomaly maps. Code is available at https://github.com/marksgraham/ddpm-ood, Comment: Accepted at MICCAI 2023

Published
2023

2. Towards Quantifying Neurovascular Resilience

Author

Moriconi, Stefano, Rehwald, Rafael, Zuluaga, Maria A., Jäger, H. Rolf, Nachev, Parashkev, Ourselin, Sébastien, Cardoso, M. Jorge, Moriconi, Stefano, Rehwald, Rafael, Zuluaga, Maria A., Jäger, H. Rolf, Nachev, Parashkev, Ourselin, Sébastien, and Cardoso, M. Jorge

Abstract

Whilst grading neurovascular abnormalities is critical for prompt surgical repair, no statistical markers are currently available for predicting the risk of adverse events, such as stroke, and the overall resilience of a network to vascular complications. The lack of compact, fast, and scalable simulations with network perturbations impedes the analysis of the vascular resilience to life-threatening conditions, surgical interventions and long-term follow-up. We introduce a graph-based approach for efficient simulations, which statistically estimates biomarkers from a series of perturbations on the patient-specific vascular network. Analog-equivalent circuits are derived from clinical angiographies. Vascular graphs embed mechanical attributes modelling the impedance of a tubular structure with stenosis, tortuosity and complete occlusions. We evaluate pressure and flow distributions, simulating healthy topologies and abnormal variants with perturbations in key pathological scenarios. These describe the intrinsic network resilience to pathology, and delineate the underlying cerebrovascular autoregulation mechanisms. Lastly, a putative graph sampling strategy is devised on the same formulation, to support the topological inference of uncertain neurovascular graphs.

Published
2019
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3. Predicting the presence of macrovascular causes in non-traumatic intracerebral haemorrhage : The DIAGRAM prediction score

Author

Hilkens, Nina A, van Asch, Charlotte J J, Werring, David J, Wilson, Duncan, Rinkel, Gabriël J E, Algra, Ale, Velthuis, Birgitta K, de Kort, Gérard A P, Witkamp, Theo D, van Nieuwenhuizen, Koen M, de Leeuw, Frank-Erik, Schonewille, Wouter J., de Kort, Paul L M, Dippel, Diederik W J, Raaymakers, Theodora W M, Hofmeijer, Jeannette, Wermer, Marieke J H, Kerkhoff, Henk, Jellema, Korné, Bronner, Irene M., Remmers, Michel J M, Bienfait, Henri Paul, Witjes, Ron J G M, Jäger, H Rolf, Greving, Jacoba P, Klijn, Catharina J M, DIAGRAM study group, Hilkens, Nina A, van Asch, Charlotte J J, Werring, David J, Wilson, Duncan, Rinkel, Gabriël J E, Algra, Ale, Velthuis, Birgitta K, de Kort, Gérard A P, Witkamp, Theo D, van Nieuwenhuizen, Koen M, de Leeuw, Frank-Erik, Schonewille, Wouter J., de Kort, Paul L M, Dippel, Diederik W J, Raaymakers, Theodora W M, Hofmeijer, Jeannette, Wermer, Marieke J H, Kerkhoff, Henk, Jellema, Korné, Bronner, Irene M., Remmers, Michel J M, Bienfait, Henri Paul, Witjes, Ron J G M, Jäger, H Rolf, Greving, Jacoba P, Klijn, Catharina J M, and DIAGRAM study group

Published
2018

4. Predicting the presence of macrovascular causes in non-traumatic intracerebral haemorrhage : The DIAGRAM prediction score

Author

Hilkens, Nina A, van Asch, Charlotte J J, Werring, David J, Wilson, Duncan, Rinkel, Gabriël J E, Algra, Ale, Velthuis, Birgitta K, de Kort, Gérard A P, Witkamp, Theo D, van Nieuwenhuizen, Koen M, de Leeuw, Frank-Erik, Schonewille, Wouter J., de Kort, Paul L M, Dippel, Diederik W J, Raaymakers, Theodora W M, Hofmeijer, Jeannette, Wermer, Marieke J H, Kerkhoff, Henk, Jellema, Korné, Bronner, Irene M., Remmers, Michel J M, Bienfait, Henri Paul, Witjes, Ron J G M, Jäger, H Rolf, Greving, Jacoba P, Klijn, Catharina J M, DIAGRAM study group, Hilkens, Nina A, van Asch, Charlotte J J, Werring, David J, Wilson, Duncan, Rinkel, Gabriël J E, Algra, Ale, Velthuis, Birgitta K, de Kort, Gérard A P, Witkamp, Theo D, van Nieuwenhuizen, Koen M, de Leeuw, Frank-Erik, Schonewille, Wouter J., de Kort, Paul L M, Dippel, Diederik W J, Raaymakers, Theodora W M, Hofmeijer, Jeannette, Wermer, Marieke J H, Kerkhoff, Henk, Jellema, Korné, Bronner, Irene M., Remmers, Michel J M, Bienfait, Henri Paul, Witjes, Ron J G M, Jäger, H Rolf, Greving, Jacoba P, Klijn, Catharina J M, and DIAGRAM study group

Published
2018

5. Predicting the presence of macrovascular causes in non-traumatic intracerebral haemorrhage : The DIAGRAM prediction score

Author

Hilkens, Nina A, van Asch, Charlotte J J, Werring, David J, Wilson, Duncan, Rinkel, Gabriël J E, Algra, Ale, Velthuis, Birgitta K, de Kort, Gérard A P, Witkamp, Theo D, van Nieuwenhuizen, Koen M, de Leeuw, Frank-Erik, Schonewille, Wouter J., de Kort, Paul L M, Dippel, Diederik W J, Raaymakers, Theodora W M, Hofmeijer, Jeannette, Wermer, Marieke J H, Kerkhoff, Henk, Jellema, Korné, Bronner, Irene M., Remmers, Michel J M, Bienfait, Henri Paul, Witjes, Ron J G M, Jäger, H Rolf, Greving, Jacoba P, Klijn, Catharina J M, DIAGRAM study group, Hilkens, Nina A, van Asch, Charlotte J J, Werring, David J, Wilson, Duncan, Rinkel, Gabriël J E, Algra, Ale, Velthuis, Birgitta K, de Kort, Gérard A P, Witkamp, Theo D, van Nieuwenhuizen, Koen M, de Leeuw, Frank-Erik, Schonewille, Wouter J., de Kort, Paul L M, Dippel, Diederik W J, Raaymakers, Theodora W M, Hofmeijer, Jeannette, Wermer, Marieke J H, Kerkhoff, Henk, Jellema, Korné, Bronner, Irene M., Remmers, Michel J M, Bienfait, Henri Paul, Witjes, Ron J G M, Jäger, H Rolf, Greving, Jacoba P, Klijn, Catharina J M, and DIAGRAM study group

Published
2018

6. Predicting the presence of macrovascular causes in non-traumatic intracerebral haemorrhage: The DIAGRAM prediction score

Author

Cardiovasculaire Epi Team 6, Circulatory Health, JC onderzoeksprogramma Cardiovasculaire Epidemiologie, Hersenen-Medisch 2, Neurologie, Brain, ZL Cerebrovasculaire Ziekten Medisch, Researchgr. Hart-brein as., Cancer, MS Radiologie, Opleiding Neurologie, Hilkens, Nina A, van Asch, Charlotte J J, Werring, David J, Wilson, Duncan, Rinkel, Gabriël J E, Algra, Ale, Velthuis, Birgitta K, de Kort, Gérard A P, Witkamp, Theo D, van Nieuwenhuizen, Koen M, de Leeuw, Frank-Erik, Schonewille, Wouter J., de Kort, Paul L M, Dippel, Diederik W J, Raaymakers, Theodora W M, Hofmeijer, Jeannette, Wermer, Marieke J H, Kerkhoff, Henk, Jellema, Korné, Bronner, Irene M., Remmers, Michel J M, Bienfait, Henri Paul, Witjes, Ron J G M, Jäger, H Rolf, Greving, Jacoba P, Klijn, Catharina J M, DIAGRAM study group, Cardiovasculaire Epi Team 6, Circulatory Health, JC onderzoeksprogramma Cardiovasculaire Epidemiologie, Hersenen-Medisch 2, Neurologie, Brain, ZL Cerebrovasculaire Ziekten Medisch, Researchgr. Hart-brein as., Cancer, MS Radiologie, Opleiding Neurologie, Hilkens, Nina A, van Asch, Charlotte J J, Werring, David J, Wilson, Duncan, Rinkel, Gabriël J E, Algra, Ale, Velthuis, Birgitta K, de Kort, Gérard A P, Witkamp, Theo D, van Nieuwenhuizen, Koen M, de Leeuw, Frank-Erik, Schonewille, Wouter J., de Kort, Paul L M, Dippel, Diederik W J, Raaymakers, Theodora W M, Hofmeijer, Jeannette, Wermer, Marieke J H, Kerkhoff, Henk, Jellema, Korné, Bronner, Irene M., Remmers, Michel J M, Bienfait, Henri Paul, Witjes, Ron J G M, Jäger, H Rolf, Greving, Jacoba P, Klijn, Catharina J M, and DIAGRAM study group

Published
2018

7. 3D multirater RCNN for multimodal multiclass detection and characterisation of extremely small objects

Author

Sudre, Carole H., Anson, Beatriz Gomez, Ingala, Silvia, Lane, Chris D., Jimenez, Daniel, Haider, Lukas, Varsavsky, Thomas, Smith, Lorna, Jäger, H. Rolf, Cardoso, M. Jorge, Sudre, Carole H., Anson, Beatriz Gomez, Ingala, Silvia, Lane, Chris D., Jimenez, Daniel, Haider, Lukas, Varsavsky, Thomas, Smith, Lorna, Jäger, H. Rolf, and Cardoso, M. Jorge

Abstract

Extremely small objects (ESO) have become observable on clinical routine magnetic resonance imaging acquisitions, thanks to a reduction in acquisition time at higher resolution. Despite their small size (usually $<$10 voxels per object for an image of more than $10^6$ voxels), these markers reflect tissue damage and need to be accounted for to investigate the complete phenotype of complex pathological pathways. In addition to their very small size, variability in shape and appearance leads to high labelling variability across human raters, resulting in a very noisy gold standard. Such objects are notably present in the context of cerebral small vessel disease where enlarged perivascular spaces and lacunes, commonly observed in the ageing population, are thought to be associated with acceleration of cognitive decline and risk of dementia onset. In this work, we redesign the RCNN model to scale to 3D data, and to jointly detect and characterise these important markers of age-related neurovascular changes. We also propose training strategies enforcing the detection of extremely small objects, ensuring a tractable and stable training process.

Published
2018

8. VTrails: Inferring Vessels with Geodesic Connectivity Trees

Author

Moriconi, Stefano, Zuluaga, Maria A., Jäger, H. Rolf, Nachev, Parashkev, Ourselin, Sébastien, Cardoso, M. Jorge, Moriconi, Stefano, Zuluaga, Maria A., Jäger, H. Rolf, Nachev, Parashkev, Ourselin, Sébastien, and Cardoso, M. Jorge

Abstract

The analysis of vessel morphology and connectivity has an impact on a number of cardiovascular and neurovascular applications by providing patient-specific high-level quantitative features such as spatial location, direction and scale. In this paper we present an end-to-end approach to extract an acyclic vascular tree from angiographic data by solving a connectivity-enforcing anisotropic fast marching over a voxel-wise tensor field representing the orientation of the underlying vascular tree. The method is validated using synthetic and real vascular images. We compare VTrails against classical and state-of-the-art ridge detectors for tubular structures by assessing the connectedness of the vesselness map and inspecting the synthesized tensor field as proof of concept. VTrails performance is evaluated on images with different levels of degradation: we verify that the extracted vascular network is an acyclic graph (i.e. a tree), and we report the extraction accuracy, precision and recall.

Published
2018
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