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Improved Automatic Bone Segmentation Using Large-Scale Simulated Ultrasound Data to Segment Real Ultrasound Bone Surface Data

Automatic segmentation of bone surfaces from ultrasound images is of great interest in the ultrasound-guided computer assisted orthopedic surgery field. These automatic segmentations help the system locate where the bone surface is in the image which can allow for proper surgical manipulation. Most recently, methods based on deep learning have achieved promising results. However, a drawback is that these methods require a large number of training dataset. Therefore, the main objective of this work is the investigation of how large-scale simulated ultrasound data can be used to improve the accuracy of deep learning-based segmentation methods. A transfer learning network and a mixed network approach were used to evaluate how well the large-scale simulated data worked to segment ultrasound bone surfaces. The Sorensen-Dice Coefficient and Average Euclidean Distance values were calculated to determine that by using simulated bone ultrasound data, the success of traditional deep learning methods increases compared to using small-scale real ultrasound data only. Initial results show improvements in segmentation performance can be obtained by combining simulated ultrasound data with in vivo ultrasound scans.