99 citations · 174 across the 11 of their papers we have counts for
11 papers
Longitudinal Vestibular Schwannoma Dataset with Consensus-based Human-in-the-loop Annotations
Navodini Wijethilake, Marina Ivory, Oscar MacCormac +17
Accurate segmentation of vestibular schwannoma (VS) on Magnetic Resonance Imaging (MRI) is essential for patient management but often requires time-intensive manual annotations by…
Beyond one-hot encoding? Journey into compact encoding for large multi-class segmentation
Aaron Kujawa, Thomas Booth, Tom Vercauteren
This work presents novel methods to reduce computational and memory requirements for medical image segmentation with a large number of classes. We curiously observe challenges in m…
Label tree semantic losses for rich multi-class medical image segmentation
Junwen Wang, Oscar MacCormac, William Rochford +3
Rich and accurate medical image segmentation is poised to underpin the next generation of AI-defined clinical practice by delineating critical anatomy for pre-operative planning, g…
Tree-based Semantic Losses: Application to Sparsely-supervised Large Multi-class Hyperspectral Segmentation
Junwen Wang, Oscar Maccormac, William Rochford +3
Hyperspectral imaging (HSI) shows great promise for surgical applications, offering detailed insights into biological tissue differences beyond what the naked eye can perceive. Ref…
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…
A generalisable head MRI defacing pipeline: Evaluation on 2,566 meningioma scans
Lorena Garcia-Foncillas Macias, Aaron Kujawa, Aya Elshalakany +2
Reliable MRI defacing techniques to safeguard patient privacy while preserving brain anatomy are critical for research collaboration. Existing methods often struggle with incomplet…
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.…
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…
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…
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…
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…