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.

Surgical tool tracking and pose estimation in retinal microsurgery

TOMBARI, FEDERICO;
2015

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.
2015
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
266
273
Rieke, Nicola; Tan, David Joseph; Alsheakhali, Mohamed; Tombari, Federico; Filippo, Chiara Amat di San; Belagiannis, Vasileios; Eslami, Abouzar; Navab, Nassir
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/554002
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