3D vertebra segmentation by feature selection active shape model

Abstract

In this paper, a former method has been adapted to perform vertebra segmentations for the 2ndWorkshop on Computational Methods and Clinical Applications for Spine Imaging (CSI 2014). A statistical Shape Models (SSM) of each lumbar vertebra was created for the segmentation step. From manually placed intervertebral discs centres, the similarity parameters are computed to initialise the vertebra shapes. The segmentation is performed by iteratively deforming amesh inside the image intensity and then projecting it into the SSM space until convergence. Afterwards, a relaxation step based on B-spline is applied to overcome the SSM rigidity. The deformation of the mesh, within the image intensity, is performed by displacing each landmark along the normal direction of the surface mesh at the landmark position seeking a minimum of a cost function based on a set of trained features. The organisers tested the performance of our method with a dataset of five patients, achieving a global mean Dice Similarity Index (DSI) of 93.4%. Results were consistent and accurate along the lumbar spine 93.8, 93.9, 93.7, 93.4 and 92.1%, from L1 to L5.