English
 
Help Privacy Policy Disclaimer
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT

Released

Conference Paper

FLEXseg: Next Generation Brain MRI Segmentation at 9.4 T

MPS-Authors
/persons/resource/persons252833

Steiglechner,  J
Department High-Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, Max Planck Society;

/persons/resource/persons251740

Wang,  Q
Department High-Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, Max Planck Society;

/persons/resource/persons272752

Ramadan,  D
Department High-Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, Max Planck Society;

/persons/resource/persons273224

Mahler,  L
Department High-Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, Max Planck Society;

/persons/resource/persons84187

Scheffler,  K
Department High-Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, Max Planck Society;

/persons/resource/persons216054

Lindig,  T
Department High-Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, Max Planck Society;

/persons/resource/persons133483

Lohmann,  G
Department High-Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, Max Planck Society;

External Resource
Fulltext (restricted access)
There are currently no full texts shared for your IP range.
Fulltext (public)
There are no public fulltexts stored in PuRe
Supplementary Material (public)
There is no public supplementary material available
Citation

Steiglechner, J., Wang, Q., Ramadan, D., Mahler, L., Scheffler, K., Bender, B., et al. (submitted). FLEXseg: Next Generation Brain MRI Segmentation at 9.4 T.


Cite as: https://hdl.handle.net/21.11116/0000-000A-6320-2
Abstract
Automatic image segmentation at magnetic resonance imaging (MRI) of the brain is
essential for a number of applications. Many well-known segmentation tools exist for the
clinical domain. However, we have found that they become unreliable when applied to
ultra-high resolution images and, in particular, to data acquired at magnetic field strength
of 9.4 T. This has motivated us to develop a segmentation method that can handle images
at ultra-high resolution ≤ 0.6 mm and field strengths 1.5–9.4 T. Specifically, we propose
an adversarial game for flexible domain adaptation of convolutional neural networks in the
context of brain MRI segmentation. In particular, we develop FLEXseg, the first brain
MRI segmentation method suitable for images acquired at 9.4 T with 0.6 mm isotropic
resolution. We demonstrate the performance of FLEXseg by comparing it with manually
corrected labels approved by expert neuroradiologists.