Samseg

Introduction

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.

SAMseg is an acronym for Sequence-Adaptive Model for Segmentation, and is based on the work of Oula Puonti, Juan Eugenio Iglesias and Koen Van Leemput: Fast and Sequence-Adaptive Whole-Brain Segmentation Using Parametric Bayesian Modeling

Work on Samseg is driven by three distinct grant aims:

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.
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.
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).

This page is intended to document the multiple fronts of work composing these aims.

Project Fronts

Evaluate existing Samseg tools - See SamsegEvaluationMarch2017
Bring Samseg tools into FS repository
Replacement of SPM registration tool - See SamsegAffine
Testing of Samseg-T1 against FS aseg - See SamsegAsegTesting
Subject acquisition

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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.  

SAMseg is an acronym for Sequence-Adaptive Model for 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]]

Work on Samseg is driven by three distinct grant aims:

 1. 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.

 2. 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.

 3. 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).

This page is intended to document the multiple fronts of work composing these aims.  

== Project Fronts ==

 * Evaluate existing Samseg tools - See [[SamsegEvaluationMarch2017]]
 * Bring Samseg tools into FS repository 
 * Replacement of SPM registration tool - See [[SamsegAffine]]
 * Testing of Samseg-T1 against FS aseg - See [[SamsegAsegTesting]]
 * Subject acquisition