Random Forest-Based Bone Segmentation in Ultrasound
Ultrasound (US) imaging is a safe alternative to radiography for guidance during minimally invasive orthopedic procedures. However, ultrasound is challenging to interpret because of the relatively low signal-to-noise ratio and its inherent speckle pattern that decreases image quality. Here we describe a method for automatic bone segmentation in 2-D ultrasound images using a patch-based random forest classifier and several ultrasound specific features, such as shadowing. We illustrate that existing shadow features are not robust to changes in US acquisition parameters, and propose a novel robust shadow feature. We evaluate the method on several US data sets and report that it favorably compares with existing techniques. We achieve a recall of 0.86 at a precision of 0.82 on a test set of 143 spinal US images.
|Keywords||Intra-operative, Machine learning, Orthopedic procedure, Spine, Ultrasound, Ultrasound guidance, Vertebra|
|Persistent URL||dx.doi.org/10.1016/j.ultrasmedbio.2017.04.022, hdl.handle.net/1765/101010|
|Journal||Ultrasound in Medicine & Biology|
Baka, N, Leenstra, S, & van Walsum, T.W. (2017). Random Forest-Based Bone Segmentation in Ultrasound. Ultrasound in Medicine & Biology. doi:10.1016/j.ultrasmedbio.2017.04.022