386 citations · 444 across the 8 of their papers we have counts for
9 papers
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…
A distance-based loss for smooth and continuous skin layer segmentation in optoacoustic images
Stefan Gerl, Johannes C. Paetzold, Hailong He +7
Raster-scan optoacoustic mesoscopy (RSOM) is a powerful, non-invasive optical imaging technique for functional, anatomical, and molecular skin and tissue analysis. However, both th…
VerSe: A Vertebrae Labelling and Segmentation Benchmark for Multi-detector CT Images
Anjany Sekuboyina, Malek E. Husseini, Amirhossein Bayat +66
Vertebral labelling and segmentation are two fundamental tasks in an automated spine processing pipeline. Reliable and accurate processing of spine images is expected to benefit cl…
DeepASL: Kinetic Model Incorporated Loss for Denoising Arterial Spin Labeled MRI via Deep Residual Learning
Cagdas Ulas, Giles Tetteh, Stephan Kaczmarz +2
Arterial spin labeling (ASL) allows to quantify the cerebral blood flow (CBF) by magnetic labeling of the arterial blood water. ASL is increasingly used in clinical studies due to…
Direct Estimation of Pharmacokinetic Parameters from DCE-MRI using Deep CNN with Forward Physical Model Loss
Cagdas Ulas, Giles Tetteh, Michael J. Thrippleton +5
Dynamic contrast-enhanced (DCE) MRI is an evolving imaging technique that provides a quantitative measure of pharmacokinetic (PK) parameters in body tissues, in which series of T1-…
Btrfly Net: Vertebrae Labelling with Energy-based Adversarial Learning of Local Spine Prior
Anjany Sekuboyina, Markus Rempfler, Jan Kukačka +4
Robust localisation and identification of vertebrae is essential for automated spine analysis. The contribution of this work to the task is two-fold: (1) Inspired by the human expe…
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…