Publications (37)
Learning joint lesion and tissue segmentation from task-specific hetero-modal datasets
Reuben Dorent, Wenqi Li, Jinendra Ekanayake +2
Brain tissue segmentation from multimodal MRI is a key building block of many neuroscience analysis pipelines. It could also play an important role in many clinical imaging scenari…
Inter Extreme Points Geodesics for End-to-End Weakly Supervised Image Segmentation
Reuben Dorent, Samuel Joutard, Jonathan Shapey +4
We introduce , a weakly supervised 3D approach to train a deep image segmentation network using particularly weak train-time annotations: only 6 extreme clicks…
Manual segmentation versus semi-automated segmentation for quantifying vestibular schwannoma volume on MRI
Hari McGrath, Peichao Li, Reuben Dorent +6
Management of vestibular schwannoma (VS) is based on tumour size as observed on T1 MRI scans with contrast agent injection. Current clinical practice is to measure the diameter of…
SegMatch: A semi-supervised learning method for surgical instrument segmentation
Meng Wei, Charlie Budd, Luis C. Garcia-Peraza-Herrera +3
Surgical instrument segmentation is recognised as a key enabler in providing advanced surgical assistance and improving computer-assisted interventions. In this work, we propose Se…
Driving Points Prediction For Abdominal Probabilistic Registration
Samuel Joutard, Reuben Dorent, Sebastien Ourselin +2
Inter-patient abdominal registration has various applications, from pharmakinematic studies to anatomy modeling. Yet, it remains a challenging application due to the morphological…
Hetero-Modal Variational Encoder-Decoder for Joint Modality Completion and Segmentation
Reuben Dorent, Samuel Joutard, Marc Modat +2
We propose a new deep learning method for tumour segmentation when dealing with missing imaging modalities. Instead of producing one network for each possible subset of observed mo…
Automatic Segmentation of Vestibular Schwannoma from T2-Weighted MRI by Deep Spatial Attention with Hardness-Weighted Loss
Guotai Wang, Jonathan Shapey, Wenqi Li +7
Automatic segmentation of vestibular schwannoma (VS) tumors from magnetic resonance imaging (MRI) would facilitate efficient and accurate volume measurement to guide patient manage…
Boundary Distance Loss for Intra-/Extra-meatal Segmentation of Vestibular Schwannoma
Navodini Wijethilake, Aaron Kujawa, Reuben Dorent +4
Vestibular Schwannoma (VS) typically grows from the inner ear to the brain. It can be separated into two regions, intrameatal and extrameatal respectively corresponding to being in…
Intraoperative Registration by Cross-Modal Inverse Neural Rendering
Maximilian Fehrentz, Mohammad Farid Azampour, Reuben Dorent +7
We present in this paper a novel approach for 3D/2D intraoperative registration during neurosurgery via cross-modal inverse neural rendering. Our approach separates implicit neural…
Learn2Reg 2024: New Benchmark Datasets Driving Progress on New Challenges
Lasse Hansen, Wiebke Heyer, Christoph GroÃbröhmer +51
Medical image registration is critical for clinical applications, and fair benchmarking of different methods is essential for monitoring ongoing progress in the field. To date, the…
Scribble-based Domain Adaptation via Co-segmentation
Reuben Dorent, Samuel Joutard, Jonathan Shapey +7
Although deep convolutional networks have reached state-of-the-art performance in many medical image segmentation tasks, they have typically demonstrated poor generalisation capabi…
CrossMoDA 2021 challenge: Benchmark of Cross-Modality Domain Adaptation techniques for Vestibular Schwannoma and Cochlea Segmentation
Reuben Dorent, Aaron Kujawa, Marina Ivory +37
Domain Adaptation (DA) has recently raised strong interests in the medical imaging community. While a large variety of DA techniques has been proposed for image segmentation, most…
Label merge-and-split: A graph-colouring approach for memory-efficient brain parcellation
Aaron Kujawa, Reuben Dorent, Sebastien Ourselin +1
Whole brain parcellation requires inferring hundreds of segmentation labels in large image volumes and thus presents significant practical challenges for deep learning approaches.…
LNQ 2023 challenge: Benchmark of weakly-supervised techniques for mediastinal lymph node quantification
Reuben Dorent, Roya Khajavi, Tagwa Idris +24
Accurate assessment of lymph node size in 3D CT scans is crucial for cancer staging, therapeutic management, and monitoring treatment response. Existing state-of-the-art segmentati…
A 3D Cross-modal Keypoint Descriptor for MR-US Matching and Registration
Daniil Morozov, Reuben Dorent, Nazim Haouchine
Intraoperative registration of real-time ultrasound (iUS) to preoperative Magnetic Resonance Imaging (MRI) remains an unsolved problem due to severe modality-specific differences i…
Why is the winner the best?
Matthias Eisenmann, Annika Reinke, Vivienn Weru +122
International benchmarking competitions have become fundamental for the comparative performance assessment of image analysis methods. However, little attention has been given to in…
Data-Driven Registration and Modeling of Brain Deformation for Image-Guided Neurosurgery: A Systematic Review
Tiago Assis, Colin P. Galvin, Joshua P. Castillo +12
Accurate compensation of brain deformation is critical for reliable image-guided neurosurgery. Surgical manipulation and tumor resection induce tissue motion, causing preoperative…
A self-supervised learning strategy for postoperative brain cavity segmentation simulating resections
Fernando Pérez-GarcÃa, Reuben Dorent, Michele Rizzi +9
Accurate segmentation of brain resection cavities (RCs) aids in postoperative analysis and determining follow-up treatment. Convolutional neural networks (CNNs) are the state-of-th…
crossMoDA Challenge: Evolution of Cross-Modality Domain Adaptation Techniques for Vestibular Schwannoma and Cochlea Segmentation from 2021 to 2023
Navodini Wijethilake, Reuben Dorent, Marina Ivory +38
The cross-Modality Domain Adaptation (crossMoDA) challenge series, initiated in 2021 in conjunction with the International Conference on Medical Image Computing and Computer Assist…
Unified Brain MR-Ultrasound Synthesis using Multi-Modal Hierarchical Representations
Reuben Dorent, Nazim Haouchine, Fryderyk Kögl +9
We introduce MHVAE, a deep hierarchical variational auto-encoder (VAE) that synthesizes missing images from various modalities. Extending multi-modal VAEs with a hierarchical laten…
Learning joint segmentation of tissues and brain lesions from task-specific hetero-modal domain-shifted datasets
Reuben Dorent, Thomas Booth, Wenqi Li +5
Brain tissue segmentation from multimodal MRI is a key building block of many neuroimaging analysis pipelines. Established tissue segmentation approaches have, however, not been de…
MedShapeNet -- A Large-Scale Dataset of 3D Medical Shapes for Computer Vision
Jianning Li, Zongwei Zhou, Jiancheng Yang +154
Prior to the deep learning era, shape was commonly used to describe the objects. Nowadays, state-of-the-art (SOTA) algorithms in medical imaging are predominantly diverging from co…
Biomedical image analysis competitions: The state of current participation practice
Matthias Eisenmann, Annika Reinke, Vivienn Weru +352
The number of international benchmarking competitions is steadily increasing in various fields of machine learning (ML) research and practice. So far, however, little is known abou…
Connecting Jensen-Shannon and Kullback-Leibler Divergences: A New Bound for Representation Learning
Reuben Dorent, Polina Golland, William Wells
Mutual Information (MI) is a fundamental measure of statistical dependence widely used in representation learning. While direct optimization of MI via its definition as a Kullback-…
The Brain Resection Multimodal Image Registration (ReMIND2Reg) 2025 Challenge
Reuben Dorent, Laura Rigolo, Colin P. Galvin +8
Accurate intraoperative image guidance is critical for achieving maximal safe resection in brain tumor surgery, yet neuronavigation systems based on preoperative MRI lose accuracy…
Learning to Match 2D Keypoints Across Preoperative MR and Intraoperative Ultrasound
Hassan Rasheed, Reuben Dorent, Maximilian Fehrentz +6
We propose in this paper a texture-invariant 2D keypoints descriptor specifically designed for matching preoperative Magnetic Resonance (MR) images with intraoperative Ultrasound (…
Permutohedral Attention Module for Efficient Non-Local Neural Networks
Samuel Joutard, Reuben Dorent, Amanda Isaac +3
Medical image processing tasks such as segmentation often require capturing non-local information. As organs, bones, and tissues share common characteristics such as intensity, sha…
Beyond the LUMIR challenge: The pathway to foundational registration models
Junyu Chen, Shuwen Wei, Joel Honkamaa +33
Medical image challenges have played a transformative role in advancing the field, catalyzing innovation and establishing new performance benchmarks. Image registration, a foundati…
SIAM: Head and Brain MRI Segmentation from Few High-Quality Templates via Synthetic Training
Romain Valabregue, Ines Khemir, Eric Badinet +3
Synthetic training has recently advanced brain MRI segmentation by enabling contrast-agnostic models trained entirely on generated data. However, most existing approaches rely on h…
Unsupervised anomaly detection using Bayesian flow networks: application to brain FDG PET in the context of Alzheimer's disease
Hugues Roy, Reuben Dorent, Ninon Burgos
Unsupervised anomaly detection (UAD) plays a crucial role in neuroimaging for identifying deviations from healthy subject data and thus facilitating the diagnosis of neurological d…
FastGeodis: Fast Generalised Geodesic Distance Transform
Muhammad Asad, Reuben Dorent, Tom Vercauteren
The FastGeodis package provides an efficient implementation for computing Geodesic and Euclidean distance transforms (or a mixture of both), targeting efficient utilisation of CPU…
A multi-organ point cloud registration algorithm for abdominal CT registration
Samuel Joutard, Thomas Pheiffer, Chloe Audigier +6
Registering CT images of the chest is a crucial step for several tasks such as disease progression tracking or surgical planning. It is also a challenging step because of the heter…
Deep Biomechanically-Guided Interpolation for Keypoint-Based Brain Shift Registration
Tiago Assis, Ines P. Machado, Benjamin Zwick +2
Accurate compensation of brain shift is critical for maintaining the reliability of neuronavigation during neurosurgery. While keypoint-based registration methods offer robustness…
Unified Cross-Modal Medical Image Synthesis with Hierarchical Mixture of Product-of-Experts
Reuben Dorent, Nazim Haouchine, Alexandra Golby +3
We propose a deep mixture of multimodal hierarchical variational auto-encoders called MMHVAE that synthesizes missing images from observed images in different modalities. MMHVAE's…
Spatiotemporal Disentanglement of Arteriovenous Malformations in Digital Subtraction Angiography
Kathleen Baur, Xin Xiong, Erickson Torio +6
Although Digital Subtraction Angiography (DSA) is the most important imaging for visualizing cerebrovascular anatomy, its interpretation by clinicians remains difficult. This is pa…
Learning Expected Appearances for Intraoperative Registration during Neurosurgery
Nazim Haouchine, Reuben Dorent, Parikshit Juvekar +5
We present a novel method for intraoperative patient-to-image registration by learning Expected Appearances. Our method uses preoperative imaging to synthesize patient-specific exp…
Patient-Specific Real-Time Segmentation in Trackerless Brain Ultrasound
Reuben Dorent, Erickson Torio, Nazim Haouchine +5
Intraoperative ultrasound (iUS) imaging has the potential to improve surgical outcomes in brain surgery. However, its interpretation is challenging, even for expert neurosurgeons.…