Y Liu, et al. Vanderbilt University, TN Journal Club Y Liu, et al. Vanderbilt University, TN “Thalamic Nuclei Segmentation in Clinical 3T T1-weighted Images Using High-Resolution 7T Shape Models” Oct 12, 2015 Jason Su

Motivation Most recent publication for thalamic nuclei segmentation Notably using high-resolution T1w images instead of DTI Shape-based methods are also applicable to our dataset (since we have 3D VTK models) Perhaps another way of creating a segmentation candidate for label fusion instead of nonlinear registration

Background At ISMRM 2014, Newton, D’Hause, and Dawant presented “Visualizing Intrathalamic Structures with Combined Use of MPRAGE and SWI at 7T” The “magic image” vs the “rainbow approach” 5 subjects, MPRAGE at 4 TIs and SWI, manual seg. Traditionally, DTI and fMRI based methods These are low resolution and lack a ground truth http://cds.ismrm.org/protected/14MPresentations/0122/

Aims Segment thalamic nuclei on conventional 3T T1w using models segmented from 7T Acquiring multiple contrasts is costly and 7T is not widely available Even WMnMPRAGE may not be easy to spread due to non-product RFB? Use shape-based methods because low intra-thalamus contrast for registration-based methods

Methods: Acquisition 7 healthy subjects (5M:2F) Philips 7T – for development of shape model Ax T1w: 0.7mm-iso, no sequence details Sag MPRAGE: 0.7mm-iso, TR=4.74, TIs=[400, 640, 960, 1120], TS=4500, fa? ETL? WMn: 1mm-iso, TR=10, TI=680, TS=6000, fa=4, ETL=200 Obl. Ax/Cor SWI: 0.24x0.24x1mm, 60 slices, fa=45 Philips 3T – what the method will segment T1w: 0.7mm-iso, TR=7.92

Methods: Acquisition 3T and 7T T1w seem to be fairly matched Each image is registered to T1w (b.1) Use all these contrasts to perform manual seg. I found this display very disorienting, relies on left and right showing the same structures Should show improved contrast of some nuclei at different TIs as well as how good their registration was, don’t think it accomplishes either

Nuclei 19 nuclei were segmented compared to our 12 or 15 Some key differences, not sure if these combinations are right AV = AVD + AM MD-Pf = MD + Pf Pul = PuMI + PuA We’re missing CeM, CL, Li, LP They’re missing LGN, MGN, RN Sth

Methods: Shape Model Want to establish vertex correspondences between training subjects’ nuclei shapes Pick a random subject as the reference atlas This leaves 5 training examples and 1 test Register whole thalamus from reference to each training example using image then surface registration Apply these to next level of substructures, then use more nonlinear and shape-based reg. Recursively do this for all nuclei Want variance of a vertex point to only be due to intersubject variability so that shape model models this

Thin Plate Splines Spline-based interpolation/transformation method Like bending a thin sheet of metal Enforce smoothness on fitted surface (integral of sq. of 2nd deriv.) Has a closed-form solution Like having K control points that you can tweak around with spline interpolation Local non-affine point correspondences and global affine parameters But how do we decide which points will go to which on the truth? i.e. the ordering of i? In-house “3D snake” algorithm

Methods: Join hierarchical modeling Only segmented each individual nuclei, need to process to get a hierarchy For each level, remove inner boundaries of group to arrive at the higher-level shape

Methods: Variation estimation Now that point correspondences have been determined, estimate intersubject variability of these points Register to reference nuclei using 7dof affine to all training samples (why not 12dof or nonlinear?) Average across training samples to get a mean shape/set of points for each nuclei Compute a covariance matrix of these points from mean Eigen-decomposition to determine the principal axes/modes of variation of points 3 axes explains 80% of shape variation Don’t we really want the eigenvectors to be tuned for modeling intra nuclei surfaces?

Methods: Variation estimation

Methods: Segmentation Using the 3T T1w images Segment thalamus using FreeSurfer Shape initialization Image and shape-based registration to reference Determine point correspondences of whole thalamus contour Then take 7dof affine registration of reference surface only? Feels like throwing away all the work we did for non-linear reg. At each nuclei level, fit for b using wtd. least squares Find the combination of eigenvectors that best fits the surface that we know Ignore errors on inner points since they’re unknown in the new subject Then this combination of eigenvectors provides the inner points Repeat recursively for heirarchy Small fudge factor TPS transformation for whole thalamus mis-alignment at the end of this procedure

Validation Do six rounds of leave-one-out cross validation One is left out because it is the reference Could have alternatively built the model many times for different reference, then maybe choose the reference that’s most similar to new subject

Results

Results

Results

Results Median Dice AV VA VLa VLP VPL Pul LGN MGN CM MD-Pf Hb MTT Manual 0.842 0.779 0.715 0.815 0.734 0.891 0.837 0.720 0.830 0.885 0.678 0.690 THOMAS 0.774 0.701 0.633 0.865 0.729 0.736 0.784 0.884 0.691 0.556

Discussion A thorough thalamic nuclei segmentation method that works with conventional 3T T1w images Notes these problems: Manual segmentation error is unquantified Limited data to capture intersubject variation No mention of registration accuracy of 7T to 3T esp. important for ground truths Can do even better with images that actually have intra-thalamic contrast