  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.
