99 citations · 369 across the 31 of their papers we have counts for
14 papers · 1 filter
Redefining Instance Matching: A Unified Framework for Part-Aware Matching in Panoptic Segmentation Evaluation
Erik Großkopf, Soumya Snigdha Kundu, Hendrik Möller +9
The Panoptic Quality (PQ) metric is the standard for jointly evaluating instance and semantic segmentation. However, its original definition relies on a One-to-One matching between…
Instance Awareness of Multi-class Semantic Segmentation Loss Functions
Soumya Snigdha Kundu, Florian Kofler, Marina Ivory +3
Instance-sensitive losses for semantic segmentation such as blob loss and CC loss were designed to address instance imbalance, ensuring small lesions generate the same gradient as…
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…
X-RAFT: Cross-Modal Non-Rigid Registration of Blue and White Light Neurosurgical Hyperspectral Images
Charlie Budd, Silvère Ségaud, Matthew Elliot +4
Integration of hyperspectral imaging into fluorescence-guided neurosurgery has the potential to improve surgical decision making by providing quantitative fluorescence measurements…
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…
UltraFlwr -- An Efficient Federated Surgical Object Detection Framework
Yang Li, Soumya Snigdha Kundu, Maxence Boels +6
Surgical object detection in laparoscopic videos enables real-time instrument identification for workflow analysis and skills assessment, but training robust models such as You Onl…
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.…
ReCAP: Recursive Cross Attention Network for Pseudo-Label Generation in Robotic Surgical Skill Assessment
Julien Quarez, Marc Modat, Sebastien Ourselin +2
In surgical skill assessment, the Objective Structured Assessments of Technical Skills (OSATS) and Global Rating Scale (GRS) are well-established tools for evaluating surgeons duri…
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…
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…
Deep Reinforcement Learning Based System for Intraoperative Hyperspectral Video Autofocusing
Charlie Budd, Jianrong Qiu, Oscar MacCormac +7
Hyperspectral imaging (HSI) captures a greater level of spectral detail than traditional optical imaging, making it a potentially valuable intraoperative tool when precise tissue d…
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…
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…