An enhanced statistical shape model for automatic feature segmentation of human vertebrae

Background & objective: Human vertebrae are analysed and measured for various purposes, including to study anatomy, to diagnose illness, and to evaluate therapies. Achieving an accurate morphological and dimensional characterisation of three-dimensional (3D) vertebrae relies on the precise recognition of their features. Traditionally, these features, lacking defined edges, have been identified manually through a time-consuming, poorly repeatable, and poorly reproducible process. To the authors’ knowledge, there is only one method published in the literature able to automatically recognise all the most important vertebral features: the algorithmic feature recognition method (AFRM). It requires a high-density point cloud as input, the presence of all morphological features, even if incomplete, and incurs high computational costs. This research aims to propose an improved version of the AFRM to overcome its limitations. Methods: The proposed approach combines the robustness of AFRM to provide the semantic segmentation of a 3D healthy human vertebra with the ability of the enhanced statistical shape model (eSSM) to transfer information among different models. Specifically, AFRM provides the semantic segmentation of the mean shape of the eSSM, while the latter transfers this information to target shapes. Results: The eSSM was developed using 20 training samples of healthy adult male L2 vertebrae. The test samples included five healthy vertebrae and four vertebras with large missing parts. None of the test shapes were included in the training set. The novel approach could accurately recognise morphological features without the constraints that affect the AFRM. Conclusion: The proposed method guarantees reliable and automated segmentation through AFRM, exploiting the eSSM's ability to provide results even when dealing with sparsely populated or partially incomplete target models, significantly reducing the computational load.