Abstract
Ultrasound (US) guidance is of increasing interest for minimally invasive procedures in orthopedics due to its safety and cost benefits. However, bone segmentation from US images remains a challenge due to the low signal to noise ratio and artifacts that hamper US images. We propose to learn the appearance of bone-soft tissue interfaces from annotated training data, and present results with two classifiers, structured forest and a cascaded logistic classifier. We evaluated the proposed methods on 143 spinal images from two datasets acquired at different sites. We achieved a segmentation recall of 0.9 and precision 0.91 for the better dataset, and a recall and precision of 0.87 and 0.81 for the combined dataset, demonstrating the potential of the framework.
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Acknowledgement
The authors were financially supported by the Dutch Science Foundation STW, project number 14542. The authors would furthermore like to thank Marcel Toorop and Eelke Bos for their help in data annotation.
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Baka, N., Leenstra, S., van Walsum, T. (2016). Machine Learning Based Bone Segmentation in Ultrasound. In: Yao, J., Vrtovec, T., Zheng, G., Frangi, A., Glocker, B., Li, S. (eds) Computational Methods and Clinical Applications for Spine Imaging. CSI 2016. Lecture Notes in Computer Science(), vol 10182. Springer, Cham. https://doi.org/10.1007/978-3-319-55050-3_2
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DOI: https://doi.org/10.1007/978-3-319-55050-3_2
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