An automated statistical shape model developmental pipeline: implications to shoulder surgery parameter

Abstract

International audienceUsing Statistical Shape Models (SSM) of human scapula (S) and humerus (H) in evaluating surgical parameters can lead to successful outcomes. This work presents an integrated pipeline for building an automated and unbiased global SSM of these bones from a set of CT scans (Sn = 27, Hn = 28). First, an intrinsic consensus shape was established using an Iterative Median Closest Point algorithm (groupwise rigid registration), eliminating the need for manual landmarking/region building that induce bias. Then a mean-virtual (Mv) shape was developed using a Coherence Point Drift method (non-rigid registration). This Mv shape was used to identically resample each of the original datasets with one-to-one correspondences through the basis (Mv estimates). SSM of S and H was derived by conducting a probabilistic Principal Component Analysis on Mv estimates using Statismo toolkit. This method was compared with 1) Expectation Maximization-Iterative Closest Point algorithm, and 2) groupwise Gaussian mixture model based registration on hippocampi data (n = 42) and performed equal to or better than these two methods based on generality, specificity and compactness criteria