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In order to reduce the total amount of radiation exposure and provide real-time guidance ultrasound has been incorporated as a potential intra-operative imaging modality into various orthopedic procedures. However, high levels of noise, various imaging artifacts, and bone boundaries appearing several millimeters in thickness hinder the success of ultrasound as an alternative imaging modality in assisting orthopedic surgery procedures. Additional difficulties are also encountered during manual operation of the ultrasound transducer during image acquisition. In this work, we proposed a combination of novel scan plane identification method, based on convolutional neural networks, and bone surface localization method. The bone surface localization approach utilizes both local phase information, a combination of three different local image phase information and signal transmission map obtained from an L1 norm based contextual regularization method. The proposed network was utilized on two different US systems and to identify five different scan planes. Validation was performed on scans obtained from 16 volunteers. The correct scan plane identification rate of over 93% has been obtained. Validation against expert segmentation achieved a mean vertebra surface localization error of 0.42 mm.

Automatic Scan Plane Identification from 2D Ultrasound for Pedicle Screw Guidance