Search Results for author: Giles Tetteh

Found 9 papers, 3 papers with code

A Deep Learning Approach to Predicting Collateral Flow in Stroke Patients Using Radiomic Features from Perfusion Images

no code implementations24 Oct 2021 Giles Tetteh, Fernando Navarro, Johannes Paetzold, Jan Kirschke, Claus Zimmer, Bjoern H. Menze

First, it is time-consuming - the clinician needs to scan through several slices of images to ascertain the region of interest before deciding on what severity grade to assign to a patient.


A distance-based loss for smooth and continuous skin layer segmentation in optoacoustic images

no code implementations10 Jul 2020 Stefan Gerl, Johannes C. Paetzold, Hailong He, Ivan Ezhov, Suprosanna Shit, Florian Kofler, Amirhossein Bayat, Giles Tetteh, Vasilis Ntziachristos, Bjoern Menze

Raster-scan optoacoustic mesoscopy (RSOM) is a powerful, non-invasive optical imaging technique for functional, anatomical, and molecular skin and tissue analysis.

Direct Estimation of Pharmacokinetic Parameters from DCE-MRI using Deep CNN with Forward Physical Model Loss

no code implementations8 Apr 2018 Cagdas Ulas, Giles Tetteh, Michael J. Thrippleton, Paul A. Armitage, Stephen D. Makin, Joanna M. Wardlaw, Mike E. Davies, Bjoern H. Menze

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-weighted images are collected following the administration of a paramagnetic contrast agent.

Time Series

DeepASL: Kinetic Model Incorporated Loss for Denoising Arterial Spin Labeled MRI via Deep Residual Learning

1 code implementation8 Apr 2018 Cagdas Ulas, Giles Tetteh, Stephan Kaczmarz, Christine Preibisch, Bjoern H. Menze

Arterial spin labeling (ASL) allows to quantify the cerebral blood flow (CBF) by magnetic labeling of the arterial blood water.


DeepVesselNet: Vessel Segmentation, Centerline Prediction, and Bifurcation Detection in 3-D Angiographic Volumes

no code implementations25 Mar 2018 Giles Tetteh, Velizar Efremov, Nils D. Forkert, Matthias Schneider, Jan Kirschke, Bruno Weber, Claus Zimmer, Marie Piraud, Bjoern H. Menze

Our experiments show that, by replacing 3-D filters with cross-hair filters in our network, we achieve over 23% improvement in speed, lower memory footprint, lower network complexity which prevents overfitting and comparable accuracy (with a Cox-Wilcoxon paired sample significance test p-value of 0. 07 when compared to full 3-D filters).

Transfer Learning

Deep-FExt: Deep Feature Extraction for Vessel Segmentation and Centerline Prediction

no code implementations12 Apr 2017 Giles Tetteh, Markus Rempfler, Bjoern H. Menze, Claus Zimmer

Feature extraction is a very crucial task in image and pixel (voxel) classification and regression in biomedical image modeling.

Classification General Classification

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