Search Results for author: David Robben

Found 16 papers, 4 papers with code

Convolutional neural networks for medical image segmentation

no code implementations17 Nov 2022 Jeroen Bertels, David Robben, Robin Lemmens, Dirk Vandermeulen

In this article, we look into some essential aspects of convolutional neural networks (CNNs) with the focus on medical image segmentation.

Classification Image Segmentation +2

DeepVoxNet2: Yet another CNN framework

1 code implementation17 Nov 2022 Jeroen Bertels, David Robben, Robin Lemmens, Dirk Vandermeulen

We know that both the CNN mapping function and the sampling scheme are of paramount importance for CNN-based image analysis.

Image Classification Image Segmentation +1

Final infarct prediction in acute ischemic stroke

no code implementations9 Nov 2022 Jeroen Bertels, David Robben, Dirk Vandermeulen, Robin Lemmens

This article focuses on the control center of each human body: the brain.

The Dice loss in the context of missing or empty labels: Introducing $Φ$ and $ε$

2 code implementations19 Jul 2022 Sofie Tilborghs, Jeroen Bertels, David Robben, Dirk Vandermeulen, Frederik Maes

We find and propose heuristic combinations of $\Phi$ and $\epsilon$ that work in a segmentation setting with either missing or empty labels.

Image Segmentation Medical Image Segmentation +1

Differentiable Deconvolution for Improved Stroke Perfusion Analysis

no code implementations31 Mar 2021 Ezequiel de la Rosa, David Robben, Diana M. Sima, Jan S. Kirschke, Bjoern Menze

We show that our approach is able to generate AIFs without any manual annotation, and hence avoiding manual rater's influences.

Lesion Segmentation

Unsupervised 3D Brain Anomaly Detection

no code implementations9 Oct 2020 Jaime Simarro, Ezequiel de la Rosa, Thijs Vande Vyvere, David Robben, Diana M. Sima

Moreover, we test the potential of the method for detecting other anomalies such as low quality images, preprocessing inaccuracies, artifacts, and even the presence of post-operative signs (such as a craniectomy or a brain shunt).

Anomaly Detection

AIFNet: Automatic Vascular Function Estimation for Perfusion Analysis Using Deep Learning

no code implementations4 Oct 2020 Ezequiel de la Rosa, Diana M. Sima, Bjoern Menze, Jan S. Kirschke, David Robben

Perfusion imaging is crucial in acute ischemic stroke for quantifying the salvageable penumbra and irreversibly damaged core lesions.

Improved inter-scanner MS lesion segmentation by adversarial training on longitudinal data

no code implementations3 Feb 2020 Mattias Billast, Maria Ines Meyer, Diana M. Sima, David Robben

A discriminator model is then trained to predict if two lesion segmentations are based on scans acquired using the same scanner type or not, achieving a 78% accuracy in this task.

Lesion Segmentation

Detection of vertebral fractures in CT using 3D Convolutional Neural Networks

no code implementations5 Nov 2019 Joeri Nicolaes, Steven Raeymaeckers, David Robben, Guido Wilms, Dirk Vandermeulen, Cesar Libanati, Marc Debois

We present a detection method to opportunistically screen spine-containing CT images for the presence of these vertebral fractures.

Perfusion parameter estimation using neural networks and data augmentation

no code implementations11 Oct 2018 David Robben, Paul Suetens

Perfusion imaging plays a crucial role in acute stroke diagnosis and treatment decision making.

Data Augmentation Decision Making

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