23 citations · 37 across the 13 of their papers we have counts for
14 papers
Where is VALDO? VAscular Lesions Detection and segmentatiOn challenge at MICCAI 2021
Carole H. Sudre, Kimberlin Van Wijnen, Florian Dubost +46
Imaging markers of cerebral small vessel disease provide valuable information on brain health, but their manual assessment is time-consuming and hampered by substantial intra- and…
Longitudinal diffusion MRI analysis using Segis-Net: a single-step deep-learning framework for simultaneous segmentation and registration
Bo Li, Wiro J. Niessen, Stefan Klein +4
This work presents a single-step deep-learning framework for longitudinal image analysis, coined Segis-Net. To optimally exploit information available in longitudinal data, this me…
Learning unbiased group-wise registration (LUGR) and joint segmentation: evaluation on longitudinal diffusion MRI
Bo Li, Wiro J. Niessen, Stefan Klein +3
Analysis of longitudinal changes in imaging studies often involves both segmentation of structures of interest and registration of multiple timeframes. The accuracy of such analysi…
Neuro4Neuro: A neural network approach for neural tract segmentation using large-scale population-based diffusion imaging
Bo Li, Marius de Groot, Rebecca M. E. Steketee +7
Subtle changes in white matter (WM) microstructure have been associated with normal aging and neurodegeneration. To study these associations in more detail, it is highly important…
When Weak Becomes Strong: Robust Quantification of White Matter Hyperintensities in Brain MRI scans
Oliver Werner, Kimberlin M. H. van Wijnen, Wiro J. Niessen +4
To measure the volume of specific image structures, a typical approach is to first segment those structures using a neural network trained on voxel-wise (strong) labels and subsequ…
A hybrid deep learning framework for integrated segmentation and registration: evaluation on longitudinal white matter tract changes
Bo Li, Wiro Niessen, Stefan Klein +4
To accurately analyze changes of anatomical structures in longitudinal imaging studies, consistent segmentation across multiple time-points is required. Existing solutions often in…
Reproducible White Matter Tract Segmentation Using 3D U-Net on a Large-scale DTI Dataset
Bo Li, Marius de Groot, Meike Vernooij +3
Tract-specific diffusion measures, as derived from brain diffusion MRI, have been linked to white matter tract structural integrity and neurodegeneration. As a consequence, there i…
Automated Lesion Detection by Regressing Intensity-Based Distance with a Neural Network
Kimberlin M. H. van Wijnen, Florian Dubost, Pinar Yilmaz +5
Localization of focal vascular lesions on brain MRI is an important component of research on the etiology of neurological disorders. However, manual annotation of lesions can be ch…
Multi-atlas image registration of clinical data with automated quality assessment using ventricle segmentation
Florian Dubost, Marleen de Bruijne, Marco Nardin +10
Registration is a core component of many imaging pipelines. In case of clinical scans, with lower resolution and sometimes substantial motion artifacts, registration can produce po…
Weakly Supervised Object Detection with 2D and 3D Regression Neural Networks
Florian Dubost, Hieab Adams, Pinar Yilmaz +6
Finding automatically multiple lesions in large images is a common problem in medical image analysis. Solving this problem can be challenging if, during optimization, the automated…
Hydranet: Data Augmentation for Regression Neural Networks
Florian Dubost, Gerda Bortsova, Hieab Adams +4
Deep learning techniques are often criticized to heavily depend on a large quantity of labeled data. This problem is even more challenging in medical image analysis where the annot…
3D Regression Neural Network for the Quantification of Enlarged Perivascular Spaces in Brain MRI
Florian Dubost, Hieab Adams, Gerda Bortsova +4
Enlarged perivascular spaces (EPVS) in the brain are an emerging imaging marker for cerebral small vessel disease, and have been shown to be related to increased risk of various ne…
GP-Unet: Lesion Detection from Weak Labels with a 3D Regression Network
Florian Dubost, Gerda Bortsova, Hieab Adams +4
We propose a novel convolutional neural network for lesion detection from weak labels. Only a single, global label per image - the lesion count - is needed for training. We train a…
Transfer Learning by Asymmetric Image Weighting for Segmentation across Scanners
Veronika Cheplygina, Annegreet van Opbroek, M. Arfan Ikram +2
Supervised learning has been very successful for automatic segmentation of images from a single scanner. However, several papers report deteriorated performances when using classif…