R. Dorent

发表

An artificial intelligence framework for automatic segmentation and volumetry of vestibular schwannomas from contrast-enhanced T1-weighted and high-resolution T2-weighted MRI.

Sebastien Ourselin, Wenqi Li, Tom Vercauteren, 2019, Journal of neurosurgery.

Inter Extreme Points Geodesics for End-to-End Weakly Supervised Image Segmentation

Sébastien Ourselin, Marc Modat, Tom Vercauteren, 2021, MICCAI.

CrossMoDA 2021 challenge: Benchmark of Cross-Modality Domain Adaptation techniques for Vestibular Schwnannoma and Cochlea Segmentation pdf

Sergio Escalera, Ben Glocker, Benoit M. Dawant, 2022, ArXiv.

Correction to: Automatic Segmentation of Vestibular Schwannoma from T2-Weighted MRI by Deep Spatial Attention with Hardness-Weighted Loss

S. Ourselin, Tom Kamiel Magda Vercauteren, Guotai Wang, 2019, Lecture Notes in Computer Science.

Artificial Intelligence Opportunities for Vestibular Schwannoma Management Using Image Segmentation and Clinical Decision Tools.

S. Ourselin, Tom Kamiel Magda Vercauteren, N. Kitchen, 2021, World neurosurgery.

CrossMoDA 2021 challenge: Benchmark of Cross-Modality Domain Adaptation techniques for Vestibular Schwnannoma and Cochlea Segmentation

Maria G. Baldeon Calisto, Jae Won Choi, B. Dawant, 2022, Medical Image Analysis.

Boundary Distance Loss for Intra-/Extra-meatal Segmentation of Vestibular Schwannoma

T. Vercauteren, J. Shapey, R. Dorent, 2022, MLCN@MICCAI.

Deep Learning for Automatic Segmentation of Vestibular Schwannoma: A Retrospective Study from Multi-Centre Routine MRI

Tom Kamiel Magda Vercauteren, N. Kitchen, S. Ourselin, 2022, medRxiv.

Manual segmentation versus semi-automated segmentation for quantifying vestibular schwannoma volume on MRI

Tom Vercauteren, Sebastien Ourselin, Sotirios Bisdas, 2020, International Journal of Computer Assisted Radiology and Surgery.

Inter Extreme Points Geodesics for Weakly Supervised Segmentation pdf

Marc Modat, Sebastien Ourselin, Tom Vercauteren, 2021, ArXiv.

CrossMoDA 2021 challenge: Benchmark of cross-modality domain adaptation techniques for vestibular schwannoma and cochlea segmentation

Maria G. Baldeon Calisto, Jae Won Choi, B. Dawant, 2022, Medical Image Anal..

Driving Points Prediction For Abdominal Probabilistic Registration

M. Modat, S. Ourselin, Tom Kamiel Magda Vercauteren, 2022, MLMI@MICCAI.

Inter Extreme Points Geodesics for End-to-End Weakly Supervised Image Segmentation

M. Modat, S. Ourselin, Tom Kamiel Magda Vercauteren, 2021, MICCAI.

Hetero-Modal Variational Encoder-Decoder for Joint Modality Completion and Segmentation

Marc Modat, Tom Vercauteren, Reuben Dorent, 2019, MICCAI.

Automated Koos Classification of Vestibular Schwannoma

S. Ourselin, Tom Kamiel Magda Vercauteren, N. Kitchen, 2022, Frontiers in Radiology.

Scribble-based Domain Adaptation via Co-segmentation

T. Vercauteren, M. Modat, S. Ourselin, 2020, MICCAI.

Why is the Winner the Best?

Helena R. Torres, Bjoern H Menze, Moi Hoon Yap, 2023, 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

Biomedical image analysis competitions: The state of current participation practice

Anne L. Martel, Helena R. Torres, Bjoern H Menze, 2022, ArXiv.

Segmentation of vestibular schwannoma from MRI, an open annotated dataset and baseline algorithm

S. Ourselin, Tom Kamiel Magda Vercauteren, N. Kitchen, 2021, Scientific Data.

Automatic Segmentation of Vestibular Schwannoma from T2-Weighted MRI by Deep Spatial Attention with Hardness-Weighted Loss pdf

Sebastien Ourselin, Guotai Wang, Wenqi Li, 2019, MICCAI.

Cross-Modality Domain Adaptation for Medical Image Segmentation

Ben Glocker, Marc Modat, Spyridon Bakas, 2021 .

Learning joint segmentation of tissues and brain lesions from task-specific hetero-modal domain-shifted datasets

Sébastien Ourselin, Tom Vercauteren, Manuel Jorge Cardoso, 2020, Medical Image Anal..

Variational Encoder-Decoder for Joint Modality Completion and Segmentation p . In Medical Image Computing and Computer Assisted Intervention – MICCAI 2019

M. Modat, S. Ourselin, Samuel Joutard, 2019 .

MedShapeNet - A Large-Scale Dataset of 3D Medical Shapes for Computer Vision

Bjoern H Menze, Moon S Kim, David G. Ellis, 2023, arXiv.org.

MedShapeNet - A Large-Scale Dataset of 3D Medical Shapes for Computer Vision

Bjoern H Menze, Moon S Kim, David G. Ellis, 2023, ArXiv.

Biomedical image analysis competitions: The state of current participation practice

Anne L. Martel, Helena R. Torres, Bjoern H Menze, 2022, arXiv.org.

SegMatch: A semi-supervised learning method for surgical instrument segmentation

Luis C. García-Peraza-Herrera, Miaojing Shi, Tom Kamiel Magda Vercauteren, 2023, 2308.05232.

Segmentation of vestibular schwannoma from MRI, an open annotated dataset and baseline algorithm

S. Ourselin, Tom Kamiel Magda Vercauteren, N. Kitchen, 2021, Scientific data.

A self-supervised learning strategy for postoperative brain cavity segmentation simulating resections

Sébastien Ourselin, Tom Vercauteren, John S. Duncan, 2021, International Journal of Computer Assisted Radiology and Surgery.

Learning joint lesion and tissue segmentation from task-specific hetero-modal datasets

Sébastien Ourselin, Wenqi Li, Tom Vercauteren, 2019, MIDL.

Patient-Specific Real-Time Segmentation in Trackerless Brain Ultrasound

Sarah F. Frisken, Nazim Haouchine, R. Dorent, 2024, 2405.09959.

Deep learning for automatic segmentation of vestibular schwannoma: a retrospective study from multi-center routine MRI

J. Shapey, R. Dorent, A. Kujawa, 2024, Frontiers in computational neuroscience.

Unified Brain MR-Ultrasound Synthesis using Multi-Modal Hierarchical Representations

Sarah F. Frisken, S. Ourselin, A. Golby, 2023, MICCAI.

LNQ 2023 challenge: Benchmark of weakly-supervised techniques for mediastinal lymph node quantification

J. Ehrhardt, R. Dorent, B. Somarouthu, 2024, 2408.10069.

ReMIND: The Brain Resection Multimodal Imaging Database

Sarah F. Frisken, N. Navab, Laura Rigolo, 2023, medRxiv.