Simultaneous Segmentation and Classification of Bone Surfaces from Ultrasound Using a Multi-feature Guided CNN
This work addresses challenges in ultrasound-guided computer-assisted orthopedic surgery, providing an incremental improvement for medical imaging applications.
The paper tackled the problem of bone surface segmentation and classification from ultrasound data, which is hindered by imaging artifacts and low signal-to-noise ratio, by proposing a multi-feature guided CNN architecture that achieved statistically significant improvements in segmentation compared to state-of-the-art methods.
Various imaging artifacts, low signal-to-noise ratio, and bone surfaces appearing several millimeters in thickness have hindered the success of ultrasound (US) guided computer assisted orthopedic surgery procedures. In this work, a multi-feature guided convolutional neural network (CNN) architecture is proposed for simultaneous enhancement, segmentation, and classification of bone surfaces from US data. The proposed CNN consists of two main parts: a pre-enhancing net, that takes the concatenation of B-mode US scan and three filtered image features for the enhancement of bone surfaces, and a modified U-net with a classification layer. The proposed method was validated on 650 in vivo US scans collected using two US machines, by scanning knee, femur, distal radius and tibia bones. Validation, against expert annotation, achieved statistically significant improvements in segmentation of bone surfaces compared to state-of-the-art.