20 citations · 74 across the 22 of their papers we have counts for
7 papers · 1 filter
OOD-SEG: Exploiting out-of-distribution detection techniques for learning image segmentation from sparse multi-class positive-only annotations
Junwen Wang, Zhonghao Wang, Oscar MacCormac +2
Despite significant advancements, segmentation based on deep neural networks in medical and surgical imaging faces several challenges, two of which we aim to address in this work.…
Scribble-Based Interactive Segmentation of Medical Hyperspectral Images
Zhonghao Wang, Junwen Wang, Charlie Budd +3
Hyperspectral imaging (HSI) is an advanced medical imaging modality that captures optical data across a broad spectral range, providing novel insights into the biochemical composit…
A self-supervised and adversarial approach to hyperspectral demosaicking and RGB reconstruction in surgical imaging
Peichao Li, Oscar MacCormac, Jonathan Shapey +1
Hyperspectral imaging holds promises in surgical imaging by offering biological tissue differentiation capabilities with detailed information that is invisible to the naked eye. Fo…
Nonrigid Reconstruction of Freehand Ultrasound without a Tracker
Qi Li, Ziyi Shen, Qianye Yang +4
Reconstructing 2D freehand Ultrasound (US) frames into 3D space without using a tracker has recently seen advances with deep learning. Predicting good frame-to-frame rigid transfor…
Analysis of the 2024 BraTS Meningioma Radiotherapy Planning Automated Segmentation Challenge
Dominic LaBella, Valeriia Abramova, Mehdi Astaraki +102
The 2024 Brain Tumor Segmentation Meningioma Radiotherapy (BraTS-MEN-RT) challenge aimed to advance automated segmentation algorithms using the largest known multi-institutional da…
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.…
Transferring Relative Monocular Depth to Surgical Vision with Temporal Consistency
Charlie Budd, Tom Vercauteren
Relative monocular depth, inferring depth up to shift and scale from a single image, is an active research topic. Recent deep learning models, trained on large and varied meta-data…