Semiautomatic segmentation of vertebrae in lateral x-rays using a conditional shape model

Rationale and Objectives. Manual annotation of the full contour of the vertebrae in lateral x-rays for subsequent morphometry is time-consuming. The standard six-point morphometry is commonly used instead, 11 has been shown that the information from the complete contour improves the quality of the study. In this article, the six landmarks are given and the vertebrae are segmented taking advantage of that information. The result is a semiautomatic system in which the full contour is found with high precision, and that only requires a radiologist to mark six points per vertebra. Materials and Methods. A shape model was built for both the six landmarks and the full contours of the vertebrae L1, L2, L3, and L4 of 142 patients. The distribution of the principal components of the full contour was then modeled as a Gaussian conditional distribution depending on the principal components of the six landmarks. The conditional mean was used as initialization for active shape model optimization, and the conditional variance was used to constrain the solution to plausible shapes. Results. The achieved point-to-line error was 0.48 mm, and 95% of the points were located within 1.36 mm of the annotated contour. The accuracy is superior to those of previously published studies, at the expense of requiring the six points to be marked. Fractures and osteophytes are well approximated by the model, although they are sometimes oversmoothed. Conclusions. The proposed method provides hence a richer description than the six points, and can be used as input for shape-based morphometry to detect and quantify vertebral deformation more accurately.