3D Approximation Of Scapula Bone Shape From 2D X-Ray Images Using Landmark-Constrained Statistical Shape Model Fitting.

ABSTRACT

Two-dimensional X-ray imaging is the dominant imaging modality in low-resource countries

despite the existence of three-dimensional (3D) imaging modalities. This is because fewer

hospitals in low-resource countries can afford the 3D imaging systems as their acquisition

and operation costs are higher. However, 3D images are desirable in a range of clinical

applications, for example surgical planning. The aim of this research was to develop a tool

for 3D approximation of scapula bone from 2D X-ray images using landmark-constrained

statistical shape model fitting. First, X-ray stereophotogrammetry was used to reconstruct the 3D coordinates of points located on 2D X-ray images of the scapula, acquired from two perspectives. A suitable calibration frame was used to map the image coordinates to their corresponding 3D realworld coordinates. The 3D point localization yielded average errors of (0.14, 0.07, 0.04) mm

in the X, Y and Z coordinates respectively, and an absolute reconstruction error of 0.19 mm.

The second phase assessed the reproducibility of the scapula landmarks reported by Ohl et al.

(2010) and Borotikar et al. (2015). Only three (the inferior angle, acromion and the coracoid

process) of the eight reproducible landmarks considered were selected as these were

identifiable from the two different perspectives required for X-ray stereophotogrammetry in

this project. For the last phase, an approximation of a scapula was produced with the aid of a statistical shape model (SSM) built from a training dataset of 84 CT scapulae. This involved

constraining an SSM to the 3D reconstructed coordinates of the selected reproducible

landmarks from 2D X-ray images. Comparison of the approximate model with a CT-derived

ground truth 3D segmented volume resulted in surface-to-surface average distances of 4.28

mm and 3.20 mm, using three and sixteen landmarks respectively. Hence, increasing the

number of landmarks produces a posterior model that makes better predictions of patientspecific

reconstructions. An average Euclidean distance of 1.35 mm was obtained between

the three selected landmarks on the approximation and the corresponding landmarks on the

CT image. Conversely, a Euclidean distance of 5.99 mm was obtained between the three

selected landmarks on the original SSM and corresponding landmarks on the CT image. The

Euclidean distances confirm that a posterior model moves closer to the CT image, hence it

reduces the search space for a more exact patient-specific 3D reconstruction by other fitting algorithms.