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Journal of Medical Robotics Research


While robot-assisted minimally invasive surgery (RMIS) procedures afford a variety of benefits over open surgery and manual laparoscopic operations (including increased tool dexterity, reduced patient pain, incision size, trauma and recovery time, and lower infection rates [1], lack of spatial awareness remains an issue. Typical laparoscopic imaging can lack sufficient depth cues and haptic feedback, if provided, rarely reflects realistic tissue-tool interactions. This work is part of a larger ongoing research effort to reconstruct 3D surfaces using multiple viewpoints in RMIS to increase visual perception. The manual placement and adjustment of multicamera systems in RMIS are nonideal and prone to error [2], and other autonomous approaches focus on tool tracking and do not consider reconstruction of the surgical scene [3,4,5]. The group's previous work investigated a novel, context-aware autonomous camera positioning method [6], which incorporated both tool location and scene coverage for multiple camera viewpoint adjustments. In this paper, the authors expand upon this prior work by implementing a streamlined deep reinforcement learning approach between optimal viewpoints calculated using the prior method [6] which encourages discovery of otherwise unobserved and additional camera viewpoints. Combining the framework and robustness of the previous work with the efficiency and additional viewpoints of the augmentations presented here results in improved performance and scene coverage promising towards real-time implementation.


3D reconstruction, deep reinforcement learning, machine vision, Robot-assisted minimally invasive surgery









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Creative Commons Attribution 4.0 International License
This work is licensed under a Creative Commons Attribution 4.0 International License.


Archived as published. Open access article.

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