1. A UTOMATIC S EGMENTATION OF T HALAMIC N UCLEI WITH STEPS L ABEL F USION J.Su 1, T. Tourdias 2, M.Saranathan 1, and B.K.Rutt 1 1 Department of Radiology, Stanford University, Stanford, CA, United States 2 Department of Neuroradiology, Bordeaux University Hospital, Bordeaux, France ISMRM 2014 E-P OSTER #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 C OMPUTER N O. 87 WMnMPRAGE template of 17 subjects at 1mm 3 Automatic segmentations (filled region) for whole thalamus and nuclei with the manual truth (yellow outline) overlaid in an MS patient.

2. Declaration of Conflict of Interest or Relationship I have no conflicts of interest to disclose with regard to the subject matter of this presentation. A UTOMATIC S EGMENTATION OF T HALAMIC N UCLEI WITH STEPS L ABEL F USION J.Su 1, T. Tourdias 2, M.Saranathan 1, and B.K.Rutt 1 1 Department of Radiology, Stanford University, Stanford, CA, United States 1 Department of Neuroradiology, Bordeaux University Hospital, Bordeaux, France ISMRM 2014 E-P OSTER #4306

3. Background White matter nulled MPRAGE (WMnMPRAGE) at 7T has enabled detailed delineation of 16 thalamic nuclei guided by the Morel atlas 1,2 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 A UTOMATIC S EGMENTATION OF T HALAMIC N UCLEI WITH STEPS L ABEL F USION ISMRM 2014 #4306 1 Tourdias et al. Neuroimage. 2013 Sep 7;84C:534-545. 2 Niemann et al. Neuroimage. 2000 Dec;12(6):601-16. Manual segmentation of thalamic nuclei from WMnMPRAGE acquisition from a normal control.

4. 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 A UTOMATIC S EGMENTATION OF T HALAMIC N UCLEI WITH STEPS L ABEL F USION ISMRM 2014 #4306

5. Scanning Methods White matter nulled MPRAGE (WMnMPRAGE) data are from two different studies with varying protocols 7T, 32ch head coil, 1mm 3 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 Sampled 16min Accelerated 5.5min A UTOMATIC S EGMENTATION OF T HALAMIC N UCLEI WITH STEPS L ABEL F USION ISMRM 2014 #4306

6. Processing Methods: Registration An axial slice from the 1mm isotropic resolution WMnMPRAGE template formed by averaging over 17 subjects A UTOMATIC S EGMENTATION OF T HALAMIC N UCLEI WITH STEPS L ABEL F USION 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

7. Background: Label Fusion Differing Opinions Trust Estimate · · · Output t1t1 t2t2 tNtN t3t3 3 Cardoso et al. Med Image Anal. 2013 Aug;17(6):671-84. 4 Warfield et al. IEEE Trans Med Imaging. 2004 Jul;23(7):903-21. A UTOMATIC S EGMENTATION OF T HALAMIC N UCLEI WITH STEPS L ABEL F USION 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 STEPS 3 builds upon STAPLE 4. 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

8. 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) A UTOMATIC S EGMENTATION OF T HALAMIC N UCLEI WITH STEPS L ABEL F USION ISMRM 2014 #4306 Pul MTT

9. Results With 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 A UTOMATIC S EGMENTATION OF T HALAMIC N UCLEI WITH STEPS L ABEL F USION ISMRM 2014 #4306 Performance of the algorithm compared to a previous multi-modal technique Median CoM Change (mm) Median DiceMedian Dice in [5] Whole Thalamus 0.433 0.910N/A AV 1.428 0.361N/A VA 1.147 0.629N/A Vla 1.180 0.485N/A VLP 0.987 0.725N/A VPL 1.612 0.534N/A Pul 1.086 0.7990.725 LGN 0.528 0.5690.405 MGN 0.661 0.4750.515 CM 0.865 0.568N/A MD 0.907 0.783N/A Hb 0.332 0.583N/A MTT 3.001 0.260N/A 5 Stough et al. Proc IEEE Int Symp Biomed Imaging. 2013:852-855.

10. Results Whole 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 A UTOMATIC S EGMENTATION OF T HALAMIC N UCLEI WITH STEPS L ABEL F USION ISMRM 2014 #4306 1 Tourdias et al. Neuroimage. 2013 Sep 7;84C:534-545.

11. Discussion & Conclusions We 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 anatomies 6 A UTOMATIC S EGMENTATION OF T HALAMIC N UCLEI WITH STEPS L ABEL F USION ISMRM 2014 #4306 6 Yushkevich et al. Neuroimage. 2010 Dec;53(4):1208-24. [Cite MICCAI winners]