Abstract
In computer-aided interventions, the visual feedback of the doctor is vital. Enhancing the relevant object will help for the perception of this feedback. In this paper, we present a learning-based labeling of the surgical scene using a depth camera (comprised of RGB and depth range sensors). The depth sensor is used for background extraction and Random Forests are used for segmenting color images. The end result is a labeled scene consisting of surgeon hands, surgical instruments and background labels. We evaluated the method by conducting 10 simulated surgeries with 5 clinicians and demonstrated that the approach provides surgeons a dissected surgical scene, enhanced visualization, and upgraded depth perception.
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Pauly, O. et al. (2014). Relevance-Based Visualization to Improve Surgeon Perception. In: Stoyanov, D., Collins, D.L., Sakuma, I., Abolmaesumi, P., Jannin, P. (eds) Information Processing in Computer-Assisted Interventions. IPCAI 2014. Lecture Notes in Computer Science, vol 8498. Springer, Cham. https://doi.org/10.1007/978-3-319-07521-1_19
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DOI: https://doi.org/10.1007/978-3-319-07521-1_19
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-07520-4
Online ISBN: 978-3-319-07521-1
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