Intra-patient Non-rigid Registration of 3D Vascular Cerebral Images. - David Robben; Dirk Smeets; Daniel Ruijters; McElory Hoffmann; Laura Antanas; Frederik Maes; Paul Suetens (CLIP, 2012)

Anatomical Labeling of the Circle of Willis Using Maximum A Posteriori Graph Matching. - David Robben; Stefan Sunaert; Vincent Thijs; Guy Willms; Frederik Maes; Paul Suetens (MICCAI (1), 2013)

A Voxel-Wise, Cascaded Classification Approach to Ischemic Stroke Lesion Segmentation. - David Robben; Daan Christiaens; Janaki Raman Rangarajan; Jaap Gelderblom; Philip Joris; Frederik Maes; Paul Suetens (Brainles@MICCAI, 2015)

Perfusion Paths: Inference of Voxelwise Blood Flow Trajectories in CT Perfusion. - David Robben; Stefan Sunaert; Vincent Thijs; Guy Willms; Frederik Maes; Paul Suetens (MICCAI (2), 2015)

Inferring brain deformation during open neurosurgery using CBCT angiography. - Niels Verheyen; David Robben; Daniel Ruijters; Vitor Mendes Pereira; Olivier Brina; Frederik Maes; Paul Suetens (ISBI, 2016)

Simultaneous segmentation and anatomical labeling of the cerebral vasculature. - David Robben; Engin Türetken; Stefan Sunaert; Vincent Thijs; Guy Willems; Pascal Fua; Frederik Maes; Paul Suetens (Medical Image Anal., 2016)

Contra-Lateral Information CNN for Core Lesion Segmentation Based on Native CTP in Acute Stroke. - Jeroen Bertels; David Robben; Dirk Vandermeulen; Paul Suetens (BrainLes@MICCAI (1), 2018)

Perfusion Parameter Estimation Using Neural Networks and Data Augmentation. - David Robben; Paul Suetens (BrainLes@MICCAI (1), 2018)

Segmentation of Head and Neck Organs-At-Risk in Longitudinal CT Scans Combining Deformable Registrations and Convolutional Neural Networks. - Liesbeth Vandewinckele; David Robben; Wouter Crijns; Frederik Maes (DLMIA/ML-CDS@MICCAI, 2018)

Clinical Implementation of DeepVoxNet for Auto-Delineation of Organs at Risk in Head and Neck Cancer Patients in Radiotherapy. - Siri Willems; Wouter Crijns; Agustina La Greca Saint-Esteven; Julie Van Der Veen; David Robben; Tom Depuydt; Sandra Nuyts; Karin Haustermans; Frederik Maes (OR 2.0/CARE/CLIP/ISIC@MICCAI, 2018)

Optimization with Soft Dice Can Lead to a Volumetric Bias. - Jeroen Bertels; David Robben; Dirk Vandermeulen; Paul Suetens (BrainLes@MICCAI (1), 2019)

Improved Inter-scanner MS Lesion Segmentation by Adversarial Training on Longitudinal Data. - Mattias Billast; Maria Inês Meyer; Diana Maria Sima; David Robben (BrainLes@MICCAI (1), 2019)

Detection of Vertebral Fractures in CT Using 3D Convolutional Neural Networks. - Joeri Nicolaes; Steven Raeymaeckers; David Robben; Guido Wilms; Dirk Vandermeulen; Cesar Libanati; Marc Debois (CSI@MICCAI, 2019)

Unsupervised 3D Brain Anomaly Detection. - Jaime Simarro Viana; Ezequiel de la Rosa; Thijs Vande Vyvere; David Robben; Diana Maria Sima (BrainLes@MICCAI (1), 2020)

Differentiable Deconvolution for Improved Stroke Perfusion Analysis. - Ezequiel de la Rosa; David Robben; Diana Maria Sima; Jan S. Kirschke; Bjoern H. Menze (MICCAI (7), 2020)

Segmentation of head-and-neck organs-at-risk in longitudinal CT scans combining deformable registrations and convolutional neural networks. - Liesbeth Vandewinckele; Siri Willems; David Robben; Julie Van Der Veen; Wouter Crijns; Sandra Nuyts; Frederik Maes (Comput. methods Biomech. Biomed. Eng. Imaging Vis., 2020)

Comparative study of deep learning methods for the automatic segmentation of lung, lesion and lesion type in CT scans of COVID-19 patients. - Sofie Tilborghs; Ine Dirks; Lucas Fidon; Siri Willems; Tom Eelbode; Jeroen Bertels; Bart Ilsen; Arne Brys; Adriana Dubbeldam; Nico Buls; Panagiotis Gonidakis; Sebastián Amador Sánchez; et al. (CoRR, 2020)

Explainable-by-design Semi-Supervised Representation Learning for COVID-19 Diagnosis from CT Imaging. - Abel Díaz Berenguer; Hichem Sahli; Boris Joukovsky; Maryna Kvasnytsia; Ine Dirks; Mitchel Alioscha-Pérez; Nikos Deligiannis; Panagiotis Gonidakis; Sebastián Amador Sánchez; Redona Brahimetaj; Evgenia Papavasileiou; Jonathan Cheung-Wai Chan; et al. (CoRR, 2020)

Prediction of final infarct volume from native CT perfusion and treatment parameters using deep learning. - 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 (Medical Image Anal., 2020)

Theoretical analysis and experimental validation of volume bias of soft Dice optimized segmentation maps in the context of inherent uncertainty. - Jeroen Bertels; David Robben; Dirk Vandermeulen; Paul Suetens (Medical Image Anal., 2021)

AIFNet: Automatic vascular function estimation for perfusion analysis using deep learning. - Ezequiel de la Rosa; Diana Maria Sima; Bjoern H. Menze; Jan S. Kirschke; David Robben (Medical Image Anal., 2021)

The Dice Loss in the Context of Missing or Empty Labels: Introducing $ varPhi $ and ε. - Sofie Tilborghs; Jeroen Bertels; David Robben; Dirk Vandermeulen; Frederik Maes (MICCAI (5), 2022)

ISLES 2022: A multi-center magnetic resonance imaging stroke lesion segmentation dataset. - Moritz Roman Hernandez Petzsche; Ezequiel de la Rosa; Uta Hanning; Roland Wiest; Waldo Enrique Valenzuela Pinilla; Mauricio Reyes 001; Maria Inês Meyer; Sook-Lei Liew; Florian Kofler; Ivan Ezhov; David Robben; Alexandre Hutton; et al. (CoRR, 2022)

The Dice loss in the context of missing or empty labels: Introducing Φ and ε. - Sofie Tilborghs; Jeroen Bertels; David Robben; Dirk Vandermeulen; Frederik Maes (CoRR, 2022)

Final infarct prediction in acute ischemic stroke. - Jeroen Bertels; David Robben; Dirk Vandermeulen; Robin Lemmens (CoRR, 2022)

Convolutional neural networks for medical image segmentation. - Jeroen Bertels; David Robben; Robin Lemmens; Dirk Vandermeulen (CoRR, 2022)

DeepVoxNet2: Yet another CNN framework. - Jeroen Bertels; David Robben; Robin Lemmens; Dirk Vandermeulen (CoRR, 2022)

A Robust Ensemble Algorithm for Ischemic Stroke Lesion Segmentation: Generalizability and Clinical Utility Beyond the ISLES Challenge. - Ezequiel de la Rosa; Mauricio Reyes 001; Sook-Lei Liew; Alexandre Hutton; Roland Wiest; Johannes Kaesmacher; Uta Hanning; Arsany Hakim; Richard Zubal; Waldo Valenzuela; David Robben; Diana Maria Sima; et al. (CoRR, 2024)

ISLES'24: Improving final infarct prediction in ischemic stroke using multimodal imaging and clinical data. - Ezequiel de la Rosa; Ruisheng Su; Mauricio Reyes 001; Roland Wiest; Evamaria Olga Riedel; Florian Kofler; Kaiyuan Yang 003; Hakim Baazaoui; David Robben; Susanne Wegener; Jan S. Kirschke; Benedikt Wiestler; et al. (CoRR, 2024)

ISLES 2024: The first longitudinal multimodal multi-center real-world dataset in (sub-)acute stroke. - Evamaria Olga Riedel; Ezequiel de la Rosa; The Anh Baran; Moritz Roman Hernandez Petzsche; Hakim Baazaoui; Kaiyuan Yang 003; David Robben; Joaquin Oscar Seia; Roland Wiest; Mauricio Reyes 001; Ruisheng Su; Claus Zimmer; et al. (CoRR, 2024)