Search Results for author:
Found 12 papers, 4 papers with code
Brain Tumor Segmentation (BraTS) Challenge 2024: Meningioma Radiotherapy Planning Automated Segmentation
The 2024 Brain Tumor Segmentation Meningioma Radiotherapy (BraTS-MEN-RT) challenge aims to advance automated segmentation algorithms using the largest known multi-institutional dataset of radiotherapy planning brain MRIs with expert-annotated target labels for patients with intact or post-operative meningioma that underwent either conventional external beam radiotherapy or stereotactic radiosurgery.
A Clinical Guideline Driven Automated Linear Feature Extraction for Vestibular Schwannoma
We propose a novel algorithm to choose and extract the most appropriate maximum linear measurement from the segmented regions based on the size of the extrameatal portion of the tumour.
MedShapeNet -- A Large-Scale Dataset of 3D Medical Shapes for Computer Vision
For the medical domain, we present a large collection of anatomical shapes (e. g., bones, organs, vessels) and 3D models of surgical instrument, called MedShapeNet, created to facilitate the translation of data-driven vision algorithms to medical applications and to adapt SOTA vision algorithms to medical problems.
Synthetic white balancing for intra-operative hyperspectral imaging
Synthetic references achieve median pixel-by-pixel errors lower than 6. 5% and produce similar reconstructions and errors to an ideal reference.
Deep Reinforcement Learning Based System for Intraoperative Hyperspectral Video Autofocusing
In addition, we performed a blinded usability trial by having two neurosurgeons compare the system with different autofocus policies, and found our novel approach to be the most favourable, making our system a desirable addition for intraoperative HSI.
Spatial gradient consistency for unsupervised learning of hyperspectral demosaicking: Application to surgical imaging
However, a demosaicking algorithm is required to fully recover the spatial and spectral information of the snapshot images.
Boundary Distance Loss for Intra-/Extra-meatal Segmentation of Vestibular Schwannoma
It can be separated into two regions, intrameatal and extrameatal respectively corresponding to being inside or outside the inner ear canal.
CrossMoDA 2021 challenge: Benchmark of Cross-Modality Domain Adaptation techniques for Vestibular Schwannoma and Cochlea Segmentation
The aim was to automatically perform unilateral VS and bilateral cochlea segmentation on hrT2 as provided in the testing set (N=137).
Deep Learning Approach for Hyperspectral Image Demosaicking, Spectral Correction and High-resolution RGB Reconstruction
Hyperspectral imaging is one of the most promising techniques for intraoperative tissue characterisation.
Inter Extreme Points Geodesics for End-to-End Weakly Supervised Image Segmentation
We introduce $\textit{InExtremIS}$, a weakly supervised 3D approach to train a deep image segmentation network using particularly weak train-time annotations: only 6 extreme clicks at the boundary of the objects of interest.
Scribble-based Domain Adaptation via Co-segmentation
Instead of requiring detailed but time-consuming annotations, scribbles on the target domain are used to perform domain adaptation.
Automatic Segmentation of Vestibular Schwannoma from T2-Weighted MRI by Deep Spatial Attention with Hardness-Weighted Loss
Automatic segmentation of vestibular schwannoma (VS) tumors from magnetic resonance imaging (MRI) would facilitate efficient and accurate volume measurement to guide patient management and improve clinical workflow.
