An automatic method for feature segmentation of human thoracic and lumbar vertebrae. (October 2021) - Di Angelo, Luca; Di Stefano, Paolo; Guardiani, Emanuele
Highlights: The method automatically recognizes features of human thoracic and lumbar vertebrae. The method needs as input a complete 3D high-density discrete model of the vertebra. Segmentation and recognition rules codify some invariant inter-subject information. The method is verified with many real vertebrae coming from 3D scanner and CT-scans. Compared to state-of-art, the method has proven to be more robust and reliable. Abstract: Background and objective: Because of the three-dimensional distribution of morphological features of human vertebrae and the whole spine, in recent years, to make more precise diagnoses and to design optimized surgical procedures, new protocols have been proposed based on analysing their three-dimensional (3D) models. In the related literature, processes of segmentation and morphological features recognition are essentially performed by a skilled operator that selects the interesting areas. So, being affected by the preparation and experience of the operator, this produces an evaluation that is poorly reproducible and repeatable for the uncertainties of a typical manual measurement process. Methods: To overcome this limitation, in this paper a new automatic method is proposed for feature segmentation and recognition of human vertebrae. The proposed computer-based method, starting from 3D high density discretized models of thoracic and lumbar vertebrae, automatically performs both the semantic and geometric segmentation of their morphological features . The segmentation and recognition rules codify some important definitions used in the traditional manual method, considering all the vertebra morphology information that is invariant inter-subject. Results: The automatic method proposed here is verified by analysing many real vertebrae, both acquired using a 3D scanner and coming from Computerized Tomography (CT) scans. The obtained results are critically discussed and compared with the traditional manual methods for vertebra analysis. The method has proven to be robust and reliable in the segmentation and recognition of morphological features of vertebrae. Furthermore, the proposed automatic method avoids the blurring of quantitative parameters get from vertebrae, resulting from poor repeatability and reproducibility of manual methods used in the state-of-the-art. Conclusions: Starting from the automatic segmentation and recognition here proposed, it is possible to automatically calculate the parameters of thoracic or lumbar vertebrae used in archaeology, medicine, or biomechanics or define their new ones.