Surgical tool tracking and pose estimation in retinal microsurgery
Rieke N, Tan DJ, Alsheakhali M, Tombari F, Di San Filippo CA, Belagiannis V, Eslami A, Navab N (2015)
Publication Type: Book chapter / Article in edited volumes
Publication year: 2015
Journal
Publisher: Springer Verlag
Series: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Book Volume: 9349
Pages Range: 266-273
DOI: 10.1007/978-3-319-24553-9_33
Abstract
Retinal Microsurgery (RM) is performed with small surgical tools which are observed through a microscope. Real-time estimation of the tool’s pose enables the application of various computer-assisted techniques such as augmented reality, with the potential of improving the clinical outcome. However, most existing methods are prone to fail in in-vivo sequences due to partial occlusions, illumination and appearance changes of the tool. To overcome these problems, we propose an algorithm for simultaneous tool tracking and pose estimation that is inspired by state-of-the-art computer vision techniques. Specifically, we introduce a method based on regression forests to track the tool tip and to recover the tool’s articulated pose. To demonstrate the performance of our algorithm, we evaluate on a dataset which comprises four real surgery sequences, and compare with the state-of-the-art methods on a publicly available dataset.
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Germany (DE)How to cite
APA:
Rieke, N., Tan, D.J., Alsheakhali, M., Tombari, F., Di San Filippo, C.A., Belagiannis, V.,... Navab, N. (2015). Surgical tool tracking and pose estimation in retinal microsurgery. In (pp. 266-273). Springer Verlag.
MLA:
Rieke, Nicola, et al. "Surgical tool tracking and pose estimation in retinal microsurgery." Springer Verlag, 2015. 266-273.
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