JOURNAL CLUBY Liu, et al. Vanderbilt University, TN

“Thalamic Nuclei Segmentation in Clinical 3T T1-weighted Images Using High-Resolution 7T Shape Models”

Oct 12, 2015Jason 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

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.

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

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 points1. Register to reference nuclei using 7dof affine to all training samples

(why not 12dof or nonlinear?)2. Average across training samples to get a mean shape/set of points for

each nuclei3. Compute a covariance matrix of these points from mean4. Eigen-decomposition to determine the principal axes/modes of

variation of points– 3 axes explains 80% of shape variation

Methods: Variation estimation

Methods: Segmentation• Using the 3T T1w images1. Segment thalamus using FreeSurfer2. Shape initialization

– Image and shape-based registration to reference– Determine point correspondences of whole thalamus contour

3. Then take 7dof affine registration of reference surface only?– Feels like throwing away all the work we did for non-linear reg.

4. 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

5. Repeat recursively for heirarchy6. 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

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 0.779 0.701 0.865 0.729 0.736 0.784 0.884 0.691 0.556

Median Dice

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