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Microsurgical procedures, such as petroclival meningioma resection, require careful surgical actions in order to remove tumor tissue, while avoiding brain and vessel damaging. Such procedures are currently performed under microscope magnification. Robotic tools are emerging in order to filter surgeons' unintended movements and prevent tools from entering forbidden regions such as vascular structures. The present work investigates the use of a handheld robotic tool (Micron) to automate vessel avoidance in microsurgery. In particular, we focused on vessel segmentation, implementing a deep-learning-based segmentation strategy in microscopy images, and its integration with a feature-based passive 3D reconstruction algorithm to obtain accurate and robust vessel position. We then implemented a virtual-fixture-based strategy to control the handheld robotic tool and perform vessel avoidance. Clay vascular phantoms, lying on a background obtained from microscopy images recorded during petroclival meningioma surgery, were used for testing the segmentation and control algorithms. When testing the segmentation algorithm on 100 different phantom images, a median Dice similarity coefficient equal to 0.96 was achieved. A set of 25 Micron trials of 80 s in duration, each involving the interaction of Micron with a different vascular phantom, were recorded, with a safety distance equal to 2 mm, which was comparable to the median vessel diameter. Micron's tip entered the forbidden region 24% of the time when the control algorithm was active. However, the median penetration depth was 16.9 μm, which was two orders of magnitude lower than median vessel diameter. Results suggest the system can assist surgeons in performing safe vessel avoidance during neurosurgical procedures.

Toward Improving Safety in Neurosurgery with an Active Handheld Instrument