1. Declaration of Conflict of Interest or Relationship Research partially supported by a grant from GE Healthcare. THOMAS: T HALAMUS O PTIMIZED M ULTI -A TLAS S EGMENTATION Jason Su 1,2, Thomas Tourdias 3, Manojkumar Saranathan 2, and Brian K. Rutt 2 1 Department of Electrical Engineering, Stanford University, Stanford, California, USA 2 Department of Radiology, Stanford University, Stanford, California, USA 3 Department of Neuroradiology, Bordeaux University Hospital, Bordeaux, France #3747

2. Background THOMAS: T HALAMUS O PTIMIZED M ULTI -A TLAS S EGMENTATION #3747 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 using a WMnMPRAGE acquisition. White matter nulled MPRAGE (WMnMPRAGE) at 7T reveals details in the thalamus Enabled detailed delineation of 15 thalamic nuclei guided by the Morel atlas 1,2

3. Background THOMAS: T HALAMUS O PTIMIZED M ULTI -A TLAS S EGMENTATION #3747 Manual segmentation of thalamic nuclei using a WMnMPRAGE acquisition. Segmentation of thalamic nuclei is an important task Diseases and disorders like Multiple Sclerosis and Essential Tremor can cause atrophy in the thalamus Helpful for surgical guidance, especially MRgFUS However, manual delineation takes up to a day. Can we make it automatic?

4. THOMAS: T HALAMUS O PTIMIZED M ULTI -A TLAS S EGMENTATION #3747 Outline Automatically segment the thalamus and its nuclei using a library of manual-defined atlases Optimize for thalamic segmentation Assess its accuracy against manual delineation Impact: Enables large-scale study of thalamic atrophy and can aid in surgical planning.

5. Scanning Methods Fully Sampled 16min Accelerated 5.5min WMnMPRAGE data are from two different studies with varying protocols 7T, 32ch head coil, 1mm 3 isotropic, TS 6s, TI 680ms, TR 10ms, α 4°, BW 12 kHz ROIs Labeled ARCTrajectory Time (min) 6 controls15No1D centric16 8 controls and 15 MS patients 121.5x1.5 2D centric, radial fan beam 5.5 THOMAS: T HALAMUS O PTIMIZED M ULTI -A TLAS S EGMENTATION #3747 29 total subjects with 14 controls, 15 patients 20 (9C: 11P) used as our atlas of priors 9 (5C:4P) reserved for validation testing

6. Registration Crop the thalamus out of the prior and target images Directly nonlinearly register the prior thalamii to the target thalamus Prior subject 1: WMnMPRAGE 12 nuclei and whole thalamus ROIs Prior subject 20: WMnMPRAGE 12 nuclei and whole thalamus ROIs Prior subject 2: WMnMPRAGE 12 nuclei and whole thalamus ROIs · · · Label fusion (PICSL MALF) Target subject: WMnMPRAGE ANTS THOMAS: T HALAMUS O PTIMIZED M ULTI -A TLAS S EGMENTATION #3747

7. Processing Methods: PICSL MALF Optimization THOMAS: T HALAMUS O PTIMIZED M ULTI -A TLAS S EGMENTATION #3747 Leave-one-out cross-validation on 20 training subjects For each nuclei, search over a 3D grid of hyper-parameters Maximize the trimean + minimum Dice overlap over the cross-validation groups Evaluate these parameters on the test set 1 week on Stanford Sherlock Cluster (sherlock.stanford.edu) 87,360 total calls of PICSL MALF

8. Results Whole thalamus and nuclei segmentations Automatic result as filled region Manual truth as outline Overlaid in an MS patient. See [1] for the abbreviation glossary 1 Tourdias et al. Neuroimage. 2013 Sep 7;84C:534-545. THOMAS: T HALAMUS O PTIMIZED M ULTI -A TLAS S EGMENTATION #3747

9. Results and Impact Performance of THOMAS compared to DTI and multi-modal techniques Median ΔCoM (mm) Median Dice Median Dice in [4] Median Dice in [5] Whole Thalamus 0.288 0.931 N/A AV 0.772 0.774 0.736N/A VA 0.984 0.701 0.869 N/A VLa 1.312 0.633 N/A VLP 0.666 0.779 N/A VPL 0.846 0.701 N/A Pul 0.758 0.865 0.8190.725 LGN 0.755 0.729 N/A0.405 MGN 0.433 0.736 N/A0.515 CM 0.440 0.784 N/A MD 0.466 0.884 0.707N/A Hb 0.384 0.691 N/A MTT 0.776 0.556 N/A 4 Ye et al. Proc SPIE. 2013 Mar 13;8669. 5 Stough et al. Proc IEEE Int Symp Biomed Imaging. 2013:852-855. This is the first work to do many things: A detailed automatic segmentation for 12 thalamic nuclei in ~30min All evaluated against ground truth manual segmentation Most previous works used DTI with unsupervised learning methods (clustering) We have demonstrated a faster acquisition with higher resolution and better segmentation performance THOMAS: T HALAMUS O PTIMIZED M ULTI -A TLAS S EGMENTATION #3747

10. Future Work Exploring several other registration architectures to improve robustness 6 Preliminary success with 3T images using whole brain registration (4-5 hours) Eliminated interpolation errors by using vector ROI models 6 Pipitone et al. Neuroimage. 2014 Nov 1;101:494-512. THOMAS: T HALAMUS O PTIMIZED M ULTI -A TLAS S EGMENTATION #3747