194 citations · 280 across the 9 of their papers we have counts for
9 papers
ISLES 2022: A multi-center magnetic resonance imaging stroke lesion segmentation dataset
Moritz Roman Hernandez Petzsche, Ezequiel de la Rosa, Uta Hanning +22
Magnetic resonance imaging (MRI) is a central modality for stroke imaging. It is used upon patient admission to make treatment decisions such as selecting patients for intravenous…
Deep Quality Estimation: Creating Surrogate Models for Human Quality Ratings
Florian Kofler, Ivan Ezhov, Lucas Fidon +14
Human ratings are abstract representations of segmentation quality. To approximate human quality ratings on scarce expert data, we train surrogate quality estimation models. We eva…
blob loss: instance imbalance aware loss functions for semantic segmentation
Florian Kofler, Suprosanna Shit, Ivan Ezhov +14
Deep convolutional neural networks (CNN) have proven to be remarkably effective in semantic segmentation tasks. Most popular loss functions were introduced targeting improved volum…
FedCostWAvg: A new averaging for better Federated Learning
Leon Mächler, Ivan Ezhov, Florian Kofler +5
We propose a simple new aggregation strategy for federated learning that won the MICCAI Federated Tumor Segmentation Challenge 2021 (FETS), the first ever challenge on Federated Le…
A Deep Learning Approach to Predicting Collateral Flow in Stroke Patients Using Radiomic Features from Perfusion Images
Giles Tetteh, Fernando Navarro, Johannes Paetzold +3
Collateral circulation results from specialized anastomotic channels which are capable of providing oxygenated blood to regions with compromised blood flow caused by ischemic injur…
A Computed Tomography Vertebral Segmentation Dataset with Anatomical Variations and Multi-Vendor Scanner Data
Hans Liebl, David Schinz, Anjany Sekuboyina +13
With the advent of deep learning algorithms, fully automated radiological image analysis is within reach. In spine imaging, several atlas- and shape-based as well as deep learning…
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…
DeepVesselNet: Vessel Segmentation, Centerline Prediction, and Bifurcation Detection in 3-D Angiographic Volumes
Giles Tetteh, Velizar Efremov, Nils D. Forkert +6
We present DeepVesselNet, an architecture tailored to the challenges faced when extracting vessel networks or trees and corresponding features in 3-D angiographic volumes using dee…
Deep-FExt: Deep Feature Extraction for Vessel Segmentation and Centerline Prediction
Giles Tetteh, Markus Rempfler, Bjoern H. Menze +1
Feature extraction is a very crucial task in image and pixel (voxel) classification and regression in biomedical image modeling. In this work we present a machine learning based fe…