no code implementations • 26 Aug 2019 • Bo Li, Marius de Groot, Meike Vernooij, Arfan Ikram, Wiro Niessen, Esther Bron
As a consequence, there is a large interest in the automatic segmentation of white matter tract in diffusion tensor MRI data.
no code implementations • 26 Aug 2019 • Bo Li, Wiro Niessen, Stefan Klein, Marius de Groot, Arfan Ikram, Meike Vernooij, Esther Bron
Registration between time-points is used either as a prior for segmentation in a subsequent time point or to perform segmentation in a common space.
1 code implementation • 1 Jul 2019 • Florian Dubost, Marleen de Bruijne, Marco Nardin, Adrian V. Dalca, Kathleen L. Donahue, Anne-Katrin Giese, Mark R. Etherton, Ona Wu, Marius de Groot, Wiro Niessen, Meike Vernooij, Natalia S. Rost, Markus D. Schirmer
In this work, we propose to automatically assess the quality of registration to an atlas in clinical FLAIR MRI scans of the brain.
no code implementations • 5 Jun 2019 • Florian Dubost, Hieab Adams, Pinar Yilmaz, Gerda Bortsova, Gijs van Tulder, M. Arfan Ikram, Wiro Niessen, Meike Vernooij, Marleen de Bruijne
For comparison, we modify state-of-the-art methods to compute attention maps for weakly supervised object detection, by using a global regression objective instead of the more conventional classification objective.
no code implementations • 12 Jul 2018 • Florian Dubost, Gerda Bortsova, Hieab Adams, M. Arfan Ikram, Wiro Niessen, Meike Vernooij, Marleen de Bruijne
The proposed method reached an intraclass correlation coefficient between ground truth and network predictions of 0. 73 on the first task and 0. 84 on the second task, only using between 25 and 30 scans with a single global label per scan for training.
no code implementations • 16 Feb 2018 • Florian Dubost, Hieab Adams, Gerda Bortsova, M. Arfan Ikram, Wiro Niessen, Meike Vernooij, Marleen de Bruijne
We propose a convolutional network regression method to quantify the extent of EPVS in the basal ganglia from 3D brain MRI.
no code implementations • 22 May 2017 • Florian Dubost, Gerda Bortsova, Hieab Adams, Arfan Ikram, Wiro Niessen, Meike Vernooij, Marleen de Bruijne
We train a regression network with a fully convolutional architecture combined with a global pooling layer to aggregate the 3D output into a scalar indicating the lesion count.