Development and model form assessment of an automatic subject-specific vertebra reconstruction method. (November 2022)

• Record Type: Journal Article
• Authors: Zhang, Dingzhong; Aoude, Ahmed; Driscoll, Mark
• Is Part Of: Computers in biology and medicine. Volume 150(2022)
• Publication Date: 2022-11
• Subjects: Deep learning -- Vertebra segmentation -- Vertebra reconstruction -- Finite element analysis -- Volumetric evaluation metrics; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2022.106158
• Languages: English
• ISSNs: 0010-4825

Abstract

Abstract: Background: Current spine models for analog bench models, surgical navigation and training platforms are conventionally based on 3D models from anatomical human body polygon database or from time-consuming manual-labelled data. This work proposed a workflow of quick and accurate subject-specific vertebra reconstruction method and quantified the reconstructed model accuracy and model form errors. Methods: Four different neural networks were customized for vertebra segmentation. To validate the workflow in clinical applications, an excised human lumbar vertebra was scanned via CT and reconstructed into 3D CAD models using four refined networks. A reverse engineering solution was proposed to obtain the high-precision geometry of the excised vertebra as gold standard. The 3D model evaluation metrics and a finite element analysis (FEA) method were designed to reflect the model accuracy and model form errors. Results: The automatic segmentation networks achieved the best Dice score of 94.20% in validation datasets. The accuracy of reconstructed models was quantified with the best 3D Dice index of 92.80%, 3D IoU of 86.56%, Hausdorff distance of 1.60 mm, and the heatmaps and histograms were used for error visualization. The FEA results showed the impact of different geometries and reflected partial surface accuracy of the reconstructed vertebra under biomechanical loads with the closest percentage error of 4.2710% compared to the gold standard model. Conclusions: In this work, a workflow of automatic subject-specific vertebra reconstruction method was proposed while the errors in geometry and FEA were quantified. Such errors should be considered when leveraging subject-specific modelling towards the development and improvement of treatments. Highlights: Automatic subject-specific vertebra reconstruction was realized using neural networks. SegNet, UNet, ResUNet and KiUNet were modified for automatic vertebra segmentation. Reverse engineering was used to reconstruct a gold standard vertebra model. 3D Dice score, 3D IoU, Hausdorff distance can quantify the error from various aspects. Finite element analysis can reflect partial surface accuracy of reconstructed vertebra.