English
 
Help Privacy Policy Disclaimer
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT
Add to Basket
  Local TagsRelease HistoryDetailsSummary

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

https://openreview.net/pdf?id=EFiFV2MSNEB
(Any fulltext)

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. (2022). FLEXseg: Next Generation Brain MRI Segmentation at 9.4 T. In Medical Imaging with Deep Learning (MIDL 2022).


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.