Segmentation of the Scaphoid Bone in Ultrasound Images

Abstract

For the percutaneous fixation of scaphoid fractures, navigated approaches have been proposed to facilitate screw placement. Based on ultrasound imaging, navigation can be carried out in a cost-effective and fast manner, furthermore avoiding harmful radiation. For this purpose, a fast and efficient architecture for the automated segmentation of scaphoid bone in ultrasound volume images is needed. Methods: For 2D segmentation of the scaphoid, two architectures are taken into account: 2D nnUNet and Deeplabv3+. These architectures are trained and evaluated on a newly created dataset consisting of 67 annotated in-vivo ultrasound volume images (4576 slice images). Results: In terms of Dice coefficient, the 2D nnUNet achieves 0.67 compared to 0.57 for the Deeplabv3+. In terms of distance metrics, the 2D nnUNet shows an average symmetric surface distance error of 0.66mm, while the Deeplabv3+ achieves 0.55mm. Conclusion: Fast and accurate segmentation of the scaphoid in ultrasound volumes is feasible. Both architectures show competitive results.

Keywords

• ultrasound imaging
• machine learning
• segmentation
• scaphoid fixation