6 papers
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…
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…
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…
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…
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…