Samseg (cross-sectional, longitudinal, MS lesions)

This functionality is available in FreeSurfer 7

Author: Koen Van Leemput

E-mail: koen [at] nmr.mgh.harvard.edu

Rather than directly contacting the author, please post your questions on this module to the FreeSurfer mailing list at freesurfer [at] nmr.mgh.harvard.edu

If you use these tools in your analysis, please cite:

Cross-sectional: Fast and sequence-adaptive whole-brain segmentation using parametric Bayesian modeling. O Puonti, JE Iglesias, K Van Leemput. Neuroimage, 143, 235-249, 2016.
Longitudinal:
MS lesions:

-  ⇤ ← Revision 10 as of 2018-03-07 13:53:01 → 
  Size: 2558
  Editor: NickSchmansky
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+   ← Revision 11 as of 2020-10-13 03:55:17 → ⇥
  Size: 1005
  Editor: KoenVanLeemput
  Comment:
-Deletions are marked like this.
+Additions are marked like this.
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-= Samseg =
+= Samseg (cross-sectional, longitudinal, MS lesions) =
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-== Introduction ==
+'''''This functionality is available in [[https://surfer.nmr.mgh.harvard.edu/fswiki/ReleaseNotes|FreeSurfer 7]]'''''
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-'''SAMseg''' is the general name of the processing stream intended to replace the subcortical segmentation stream in FreeSurfer, including some of the intensity correction and skull stripping steps preceding mri_ca_register/label.
+''Author: Koen Van Leemput''
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-SAMseg is an acronym for Sequence-Adaptive Multimodal Segmentation, and is based on the work of Oula Puonti, Juan Eugenio Iglesias and Koen Van Leemput: [[http://orbit.dtu.dk/files/127427974/Fast_and_sequence_adaptive.pdf|Fast and Sequence-Adaptive Whole-Brain Segmentation Using Parametric Bayesian Modeling]] and [[http://www.sciencedirect.com/science/MiamiMultiMediaURL/1-s2.0-S1053811916304724/1-s2.0-S1053811916304724-mmc1.pdf/272508/html/S1053811916304724/67aa91bd097a5d4207be6a12a0f9b7a6/mmc1.pdf|supplementary material]]
+''E-mail: koen [at] nmr.mgh.harvard.edu''
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-Work on Samseg is driven by three distinct grant aims:
+''Rather than directly contacting the author, please post your questions on this module to the FreeSurfer mailing list at freesurfer [at] nmr.mgh.harvard.edu''
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-. Doug Greve, FreeSurfer Maintenance R01: Replacement of the current FreeSurfer segmentation stream with one that is faster, and most importantly accepts multimodal data.  This grant is also funding acquisition of subject data to compose a new multi-modal, labeled atlas, in addition to algorithm development.
+If you use these tools in your analysis, please cite:
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-. CorticoMetrics, Morphometry Phase II SBIR: Replacement of the current FS seg stream with one that is faster, but still based solely on T1-weighted input, and using the existing FS atlas.  As such, it is a technical milestone of the Maintenance grant, but one conducted by CorticoMetrics in its grant with the sole focus on speed of execution relative to current freesurfer.
+ * Cross-sectional: [[http://nmr.mgh.harvard.edu/~koen/PuontiNI2016.pdf|Fast and sequence-adaptive whole-brain segmentation using parametric Bayesian modeling]]. O Puonti, JE Iglesias, K Van Leemput. Neuroimage, 143, 235-249, 2016.
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-. Andre van der Kouwe, FS on-the-scanner (future submission): The ability to generate structure segmentations in near-real-time on an MRI scanner (or GPU'd extension), possibly accepting lower-res data and less-than current FS quality segmentations (in the name of identifying FOV of structures to acquire in a scan session).
+ * Longitudinal:
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-This page is intended to document the multiple fronts of work composing these aims.
+ * MS lesions:
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-== Project Fronts ==
+See also: ThalamicNuclei, HippocampalSubfieldsAndNucleiOfAmygdala, BrainstemSubstructures
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- * Evaluate existing Samseg tools - See [[SamsegEvaluationMarch2016]]
 * Bring Samseg tools into FS repository - See [[SamsegBuildAndRun]]
 * Replacement of SPM registration tool - See [[SamsegAffine]]
 * Testing of Samseg-T1 against FS aseg - See [[SamsegTesting]]
 * Python port: matlab and C++ to Python - [[SamsegPython]]
 * Parameter tuning: [[SamsegTesting/Tuning]]
 * Subject acquisition
+<<BR>>