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Toward Improving Safety in Neurosurgery with an Active Handheld Instrument

Overview of attention for article published in Annals of Biomedical Engineering, July 2018
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Title
Toward Improving Safety in Neurosurgery with an Active Handheld Instrument
Published in
Annals of Biomedical Engineering, July 2018
DOI 10.1007/s10439-018-2091-x
Pubmed ID
Authors

Sara Moccia, Simone Foti, Arpita Routray, Francesca Prudente, Alessandro Perin, Raymond F. Sekula, Leonardo S. Mattos, Jeffrey R. Balzer, Wendy Fellows-Mayle, Elena De Momi, Cameron N. Riviere

Abstract

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.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 62 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 62 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 10 16%
Student > Bachelor 7 11%
Student > Ph. D. Student 6 10%
Student > Doctoral Student 4 6%
Student > Master 4 6%
Other 8 13%
Unknown 23 37%
Readers by discipline Count As %
Engineering 14 23%
Medicine and Dentistry 9 15%
Computer Science 6 10%
Neuroscience 2 3%
Agricultural and Biological Sciences 1 2%
Other 5 8%
Unknown 25 40%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 09 August 2018.
All research outputs
#17,730,887
of 25,988,468 outputs
Outputs from Annals of Biomedical Engineering
#1,095
of 1,255 outputs
Outputs of similar age
#223,083
of 342,460 outputs
Outputs of similar age from Annals of Biomedical Engineering
#1
of 1 outputs
Altmetric has tracked 25,988,468 research outputs across all sources so far. This one is in the 21st percentile – i.e., 21% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,255 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.9. This one is in the 11th percentile – i.e., 11% of its peers scored the same or lower than it.
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