Inferring 3D finger bone shapes from 2D images – a detailed analysis of shape accuracy

Abstract

3D visualisation and modelling of anatomical structures of the human body play a significant role in diagnosis, computer-aided surgery, surgical planning, and patient follow-up. However, 2D X-ray images are often used in clinical routine. We propose and validate a method for reconstructing 3D shapes from 2D X-ray scans. This method comprises automatic segmentation and labelling, automated construction of 3D statistical shape models (SSM), and automatic fitting of the SSM to standard 2D X-ray images. This workflow is applied to finger bone shape reconstruction and validated for each finger bone using a set of five synthetic reference configurations and 34 CT/X-ray data pairs. We reached submillimetre accuracy for 91.59% of the synthetic data, while 79.65% of the clinical cases show surface errors below 2 mm. Thus, applying the proposed method can add valuable 3D information where 3D imaging is not indicated. Moreover, 3D imaging can be avoided if the 2D-3D reconstruction accuracy is sufficient.

Keywords

• Statistical shape model
• 2D-3D reconstruction
• image-based geometric modelling
• shape reconstruction