Evaluation of a deep learning approach for the segmentation of brain tissues and white matter hyperintensities of presumed vascular origin in MRI

Autor: Pim Moeskops, Jeroen de Bresser, Hugo J. Kuijf, Adriënne M. Mendrik, Geert Jan Biessels, Josien P.W. Pluim, Ivana Išgum

Publikováno v: NeuroImage: Clinical, Vol 17, Iss , Pp 251-262 (2018)

Automatic segmentation of brain tissues and white matter hyperintensities of presumed vascular origin (WMH) in MRI of older patients is widely described in the literature. Although brain abnormalities and motion artefacts are common in this age group

Externí odkaz: https://doaj.org/article/eedf30ac64da464894da99e7c7ad06e3

The Data Representativeness Criterion: Predicting the Performance of Supervised Classification Based on Data Set Similarity

Autor: Adriënne M. Mendrik, Rens van de Schoot, Duco Veen, Wouter M. Kouw, Evelien Schat

Publikováno v: PLoS ONE, Vol 15, Iss 8, p e0237009 (2020)
PLoS ONE
PLoS ONE, 15(8):e0237009. Public Library of Science

In a broad range of fields it may be desirable to reuse a supervised classification algorithm and apply it to a new data set. However, generalization of such an algorithm and thus achieving a similar classification performance is only possible when t

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::89322265f97c89d5a13c94891a4135df
http://arxiv.org/abs/2002.12105

Learning An Mr Acquisition-Invariant Representation Using Siamese Neural Networks

Autor: Marco Loog, Adriënne M. Mendrik, Wilbert Bartels, Wouter M. Kouw

Publikováno v: 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019).

Generalization of voxelwise classifiers is hampered by differences between MRI-scanners, e.g. different acquisition protocols and field strengths. To address this limitation, we propose a Siamese neural network (MRAI-NET) that extracts acquisition-in

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::00830aa168c7eaa6f1799ebd28f8df6b
https://doi.org/10.1109/isbi.2019.8759281

Automatic segmentation of MR brain images with a convolutional neural network

Autor: Max A. Viergever, Manon J. N. L. Benders, Adriënne M. Mendrik, Ivana Išgum, Pim Moeskops, Linda S. de Vries

Publikováno v: IEEE transactions on medical imaging, 35(5), 1252-1261. Institute of Electrical and Electronics Engineers Inc.
IEEE Transactions on Medical Imaging, 35(5), 1252-1261. Institute of Electrical and Electronics Engineers

Automatic segmentation in MR brain images is important for quantitative analysis in large-scale studies with images acquired at all ages. This paper presents a method for the automatic segmentation of MR brain images into a number of tissue classes u

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9425753aaff78b5da20612fba57de56d
https://doi.org/10.1109/tmi.2016.2548501

Evaluation of a deep learning approach for the segmentation of brain tissues and white matter hyperintensities of presumed vascular origin in MRI

Autor: Jeroen de Bresser, Ivana Išgum, Adriënne M. Mendrik, Hugo J. Kuijf, Geert Jan Biessels, Pim Moeskops, Josien P. W. Pluim

Publikováno v: NeuroImage: Clinical, 17, 251-262. Elsevier
NeuroImage. Clinical, 17, 251-262. Elsevier BV
Neuroimage: Clinical [E], 17, 251. Elsevier BV
NeuroImage : Clinical
NeuroImage: Clinical, Vol 17, Iss, Pp 251-262 (2018)

Automatic segmentation of brain tissues and white matter hyperintensities of presumed vascular origin (WMH) in MRI of older patients is widely described in the literature. Although brain abnormalities and motion artefacts are common in this age group

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f069f3ddba1f886c11991f3ab2b3e7c9
https://research.tue.nl/nl/publications/137af335-e7f6-41d3-8e28-4a1ad64e2bc4