Automatic Segmentation of Thalamic Nuclei with STEPS Label Fusion

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


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


Processing Methods: Registration

An axial slice from the 1mm isotropic resolution WMnMPRAGE template

formed by averaging over 17 subjects


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.


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)


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


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


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


6Yushkevich et al. Neuroimage. 2010 Dec;53(4):1208-24. [Cite MICCAI winners]

Documents

Automatic Segmentation of Thalamic Nuclei with STEPS Label Fusion