Group-wise Registration in NAMIC-kit Serdar K Balci (MIT) Lilla Zöllei (MGH) Kinh Tieu (BWH) Mert R Sabuncu (MIT) Polina Golland (MIT)

Robust Group-wise Registration Entropy based group-wise registration ITK implementation Empirical Evaluation 2

Background: Groupwise Registration Images Transforms Transforms: – Affine – Non-rigid using B-Splines 3

Registering to the Mean of the Population 4

Groupwise Registration: Congealing L. Zöllei, E. Learned-Miller, E. Grimson, W.M. Wells III. "Efficient Population Registration of 3D Data." 5

Congealing: Intuition If Gaussian If also Registering to the mean with LS metric 6

Implementation ITK classes – Group-wise registration using congealing Variance Entropy 7

Results Before Affine BS 4 BS 8 BS 16 Entropy Variance 8

Overlap Measures 9

Full Term Babies Before Affine BS 4 BS 8 BS 16 BS 32 10

Pre Term Babies Before Affine BS 4 BS 8 BS 16 11

Summary Implemented group-wise registration in ITK – Congealing: Entropy based registration – Affine and BSpline – Multithreaded implementation – *Bspline optimization Initial Evaluation – A population of 50 subjects – Used segmentation labels to evaluate 12

Ongoing Work Finding optimal parameters B-Spline mesh size, # of hierarchical levels Subsampling Quantitative comparison to other methods Pair-wise registration to the mean using MI 13

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Congealing with Two Images Using Parzen windows As we only have two images Pairwise registration using LS metric 15

Groupwise Reg. using Pairwise Reg. If we assume that images are independent given a subject, representative of the population 16

Registering to the mean We assume independence over images and draw i.i.d. samples from each image 17

Groupwise Registration using Joint Entropy Assume i.i.d over space, but don’t make any assumptions about images Estimating entropy of an N-dimensional distribution is a challenging task 18

Results: Congealing with Entropy Before After (B-Splines ~20mm) 19