390 citations · 826 across the 15 of their papers we have counts for
7 papers · 1 filter
Discriminating Distal Ischemic Stroke from Seizure-Induced Stroke Mimics Using Dynamic Susceptibility Contrast MRI
Marijn Borghouts, Richard McKinley, Manuel Köstner +3
Distinguishing acute ischemic strokes (AIS) from stroke mimics (SMs), particularly in cases involving medium and small vessel occlusions, remains a significant diagnostic challenge…
CortexMorph: fast cortical thickness estimation via diffeomorphic registration using VoxelMorph
Richard McKinley, Christian Rummel
The thickness of the cortical band is linked to various neurological and psychiatric conditions, and is often estimated through surface-based methods such as Freesurfer in MRI stud…
Leveraging SO(3)-steerable convolutions for pose-robust semantic segmentation in 3D medical data
Ivan Diaz, Mario Geiger, Richard Iain McKinley
Convolutional neural networks (CNNs) allow for parameter sharing and translational equivariance by using convolutional kernels in their linear layers. By restricting these kernels…
QU-BraTS: MICCAI BraTS 2020 Challenge on Quantifying Uncertainty in Brain Tumor Segmentation - Analysis of Ranking Scores and Benchmarking Results
Raghav Mehta, Angelos Filos, Ujjwal Baid +89
Deep learning (DL) models have provided state-of-the-art performance in various medical imaging benchmarking challenges, including the Brain Tumor Segmentation (BraTS) challenges.…
Are we using appropriate segmentation metrics? Identifying correlates of human expert perception for CNN training beyond rolling the DICE coefficient
Florian Kofler, Ivan Ezhov, Fabian Isensee +16
Metrics optimized in complex machine learning tasks are often selected in an ad-hoc manner. It is unknown how they align with human expert perception. We explore the correlations b…
Uncertainty-driven refinement of tumor-core segmentation using 3D-to-2D networks with label uncertainty
Richard McKinley, Micheal Rebsamen, Katrin Daetwyler +3
The BraTS dataset contains a mixture of high-grade and low-grade gliomas, which have a rather different appearance: previous studies have shown that performance can be improved by…
Stratify or Inject: Two Simple Training Strategies to Improve Brain Tumor Segmentation
Raphael Meier, Michael Rebsamen, Urspeter Knecht +3
Deep learning methods for brain tumor segmentation are typically trained in an ad hoc fashion on all available data. Brain tumors are tremendously heterogeneous in image appearance…