automatic segmentation of thalamic nuclei with steps label fusion
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AUTOMATIC SEGMENTATION OF THALAMIC NUCLEI WITH STEPS LABEL FUSION J.Su1, T. Tourdias2, M.Saranathan1, and B.K.Rutt11Department of Radiology, Stanford University, Stanford, CA, United States2Department of Neuroradiology, Bordeaux University Hospital, Bordeaux, France
ISMRM 2014 E-POSTER #4306
29 (19 training, 10 test) subjects at 7T
12 nuclei and whole thalamus manually outlined
Training priors registered and combined via label fusion
Evaluated in test group against manual truth
COMPUTER NO. 87
WMnMPRAGE template of 17 subjects at 1mm3
Automatic segmentations (filled region) for whole thalamus and nuclei with the manual truth (yellow outline) overlaid in an MS patient.
Declaration of Conflict of Interest or RelationshipI have no conflicts of interest to disclose with regard to the subject matter of this presentation.
AUTOMATIC SEGMENTATION OF THALAMIC NUCLEI WITH STEPS LABEL FUSION J.Su1, T. Tourdias2, M.Saranathan1, and B.K.Rutt1
1Department of Radiology, Stanford University, Stanford, CA, United States1Department of Neuroradiology, Bordeaux University Hospital, Bordeaux, France
ISMRM 2014 E-POSTER #4306
BackgroundWhite matter nulled MPRAGE (WMnMPRAGE) at 7T has enabled detailed delineation of 16 thalamic nuclei guided by the Morel atlas1,2
Automatic segmentation of thalamic nuclei would be an invaluable tool for the study of thalamic atrophy by diseases and potentially guided surgery
Label fusion methods with image registration can segment a new subject using an atlas of prior ROIs
AUTOMATIC SEGMENTATION OF THALAMIC NUCLEI WITH STEPS LABEL FUSION ISMRM 2014 #4306
1Tourdias et al. Neuroimage. 2013 Sep 7;84C:534-545. 2Niemann et al. Neuroimage. 2000 Dec;12(6):601-16.
Manual segmentation of thalamic nuclei from WMnMPRAGE acquisition from a
normal control.
Purpose
Assess its accuracy against the manual truth with the Dice coefficient
Optimize the technique for thalamic segmentation
Automatically segment the whole thalamus and its nuclei using a library of manual-defined ROIs
AUTOMATIC SEGMENTATION OF THALAMIC NUCLEI WITH STEPS LABEL FUSION ISMRM 2014 #4306
Scanning MethodsWhite matter nulled MPRAGE (WMnMPRAGE) data are from two different studies with varying protocols
• 7T, 32ch head coil, 1mm3 isotropic, TS 6000ms, TI 680ms, TR 10ms, α 4°, BW 12 kHz
• 6 controls scanned using unaccelerated, 1D-centric-ordered (16 min)• 16 nuclei were manually identified
• 8 controls and 15 MS patients w/ARC 1.5x1.5, 2D-centric-ordered (radial fan-beam, 5.5 min)• Only 13 nuclei could be identified
29 total subjects with 14 controls, 15 patients
Fully Sampled16min
Accelerated5.5min
AUTOMATIC SEGMENTATION OF THALAMIC NUCLEI WITH STEPS LABEL FUSION ISMRM 2014 #4306
Processing Methods: Registration
An axial slice from the 1mm isotropic resolution WMnMPRAGE template
formed by averaging over 17 subjects
AUTOMATIC SEGMENTATION OF THALAMIC NUCLEI WITH STEPS LABEL FUSION ISMRM 2014 #4306
A mean brain template was created from 17 (6C:11P) subjects in the MS study group• N4 bias field correction used to compensate for some B1- and B1+
inhomogeneities• ANTS with its default parameters for template creation
Convergence after 16 iterations
• Cortical registration was challenging, as usual• Preserves excellent detail in the thalamus
Subjects are registered to one another via the template• Warp to the template, then take the inverse warp to the target subject• Reduces 20 nonlinear registrations to 1
Background: Label FusionDifferingOpinions
TrustEstimate
· · ·
Output
t1
t2
tN
t3
3Cardoso et al. Med Image Anal. 2013 Aug;17(6):671-84.4Warfield et al. IEEE Trans Med Imaging. 2004 Jul;23(7):903-21.
AUTOMATIC SEGMENTATION OF THALAMIC NUCLEI WITH STEPS LABEL FUSION ISMRM 2014 #4306
At each voxel, need to make a decision from many differing opinions• Simple solution: take a majority vote• But we know something: our opinions come
from prior labels that have been registered to the new target subject
STEPS3 builds upon STAPLE4.At each voxel:• Keep the top locally registered prior labels• Estimate the quality of these priors, i.e.
how much we trust its segmentation• Derive the probability that this voxel is in
the ROI based on all the opinions• Threshold at 50% likelihood
Processing Methods: STEPS Optimization
STEPS has control parameters that need to be optimized
• σ, the Gaussian kernel size• Measure local registration
in a window with normalized cross-correlation
• X, the number of locally well-registered priors to use
Use cross-validation to search over the parameter space
• 29 data sets split into 20 for training and 9 for testing• The subjects used for the
template are put in the training set to avoid bias in the validation
• Maximize the mean Dice overlap for each ROI
• 44,200 total calls of STEPS• 20 hours on Stanford
Sherlock Cluster(sherlock.stanford.edu)
AUTOMATIC SEGMENTATION OF THALAMIC NUCLEI WITH STEPS LABEL FUSION ISMRM 2014 #4306
Pul
MTT
ResultsWith the per-ROI optimized parameters, we validate using the test data• Produce automatic segmentations in 9 subjects
using manual priors from 20 others
Evaluate the automatic technique vs. manual tracing
• Distance between centers of mass for each ROI• Dice coefficient
AUTOMATIC SEGMENTATION OF THALAMIC NUCLEI WITH STEPS LABEL FUSION ISMRM 2014 #4306
Performance of the algorithm compared to a previous multi-modal technique
MedianCoM Change (mm) Median Dice Median Dice in [5]
WholeThalamus 0.433 0.910 N/A
AV 1.428 0.361 N/AVA 1.147 0.629 N/AVla 1.180 0.485 N/AVLP 0.987 0.725 N/AVPL 1.612 0.534 N/APul 1.086 0.799 0.725LGN 0.528 0.569 0.405
MGN 0.661 0.475 0.515CM 0.865 0.568 N/AMD 0.907 0.783 N/AHb 0.332 0.583 N/A
MTT 3.001 0.260 N/A
5Stough et al. Proc IEEE Int Symp Biomed Imaging. 2013:852-855.
ResultsWhole thalamus and nuclei segmentations
Automatic result as filled region
Manual truth as yellow outline
Overlaid in an MS patient.
See [1] for the abbreviation glossary
AUTOMATIC SEGMENTATION OF THALAMIC NUCLEI WITH STEPS LABEL FUSION ISMRM 2014 #4306
1Tourdias et al. Neuroimage. 2013 Sep 7;84C:534-545.
Discussion & ConclusionsWe achieve accuracy in estimating the center of mass ≈1mm for most nuclei
Whole thalamic segmentation is excellent
• Nuclei segmentation varies, with the best ones being suitable as a starting point for reduced manual editing
Correction of label fusion using machine learning has been a been a highly successful combination in other anatomies6
AUTOMATIC SEGMENTATION OF THALAMIC NUCLEI WITH STEPS LABEL FUSION ISMRM 2014 #4306
6Yushkevich et al. Neuroimage. 2010 Dec;53(4):1208-24. [Cite MICCAI winners]
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