Automatic segmentation of the midfacial bone surface from ultrasound images using deep learning methods.
Authors
Affiliations (4)
Affiliations (4)
- Department of Oral and Maxillofacial Surgery, Peking University School and Hospital of Stomatology, Beijing, China; National Center for Stomatology, Beijing, China; National Clinical Research Center for Oral Diseases, Beijing, China; National Engineering Research Center of Oral Biomaterials and Digital Medical Devices, Beijing, China.
- School of Mechanical Engineering and Automation, Beihang University, Beijing, China.
- Department of Ultrasound, Peking University Third Hospital, Beijing, China.
- Department of Oral and Maxillofacial Surgery, Peking University School and Hospital of Stomatology, Beijing, China; National Center for Stomatology, Beijing, China; National Clinical Research Center for Oral Diseases, Beijing, China; National Engineering Research Center of Oral Biomaterials and Digital Medical Devices, Beijing, China. Electronic address: [email protected].
Abstract
With developments in computer science and technology, great progress has been made in three-dimensional (3D) ultrasound. Recently, ultrasound-based 3D bone modelling has attracted much attention, and its accuracy has been studied for the femur, tibia, and spine. The use of ultrasound allows data for bone surface to be acquired non-invasively and without radiation. Freehand 3D ultrasound of the bone surface can be roughly divided into two steps: segmentation of the bone surface from two-dimensional (2D) ultrasound images and 3D reconstruction of the bone surface using the segmented images. The aim of this study was to develop an automatic algorithm to segment the midface bone surface from 2D ultrasound images based on deep learning methods. Six deep learning networks were trained (nnU-Net, U-Net, ConvNeXt, Mask2Former, SegFormer, and DDRNet). The performance of the algorithms was compared with that of the ground truth and evaluated by Dice coefficient (DC), intersection over union (IoU), 95th percentile Hausdorff distance (HD95), average symmetric surface distance (ASSD), precision, recall, and time. nnU-Net yielded the highest DC of 89.3% ± 13.6% and the lowest ASSD of 0.11 ± 0.40 mm. This study showed that nnU-Net can automatically and effectively segment the midfacial bone surface from 2D ultrasound images.