Journal article
Real-time vessel segmentation and reconstruction for virtual fixtures for an active handheld microneurosurgical instrument
International journal for computer assisted radiology and surgery, v 17(6), pp 1069-1077
01 Jun 2022
PMID: 35296950
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
Complications related to vascular damage such as intra-operative bleeding may be avoided during neurosurgical procedures such as petroclival meningioma surgery. To address this and improve the patient's safety, we designed a real-time blood vessel avoidance strategy that enables operation on deformable tissue during petroclival meningioma surgery using Micron, a handheld surgical robotic tool.
We integrated real-time intra-operative blood vessel segmentation of brain vasculature using deep learning, with a 3D reconstruction algorithm to obtain the vessel point cloud in real time. We then implemented a virtual-fixture-based strategy that prevented Micron's tooltip from entering a forbidden region around the vessel, thus avoiding damage to it.
We achieved a median Dice similarity coefficient of 0.97, 0.86, 0.87 and 0.77 on datasets of phantom blood vessels, petrosal vein, internal carotid artery and superficial vessels, respectively. We conducted trials with deformable clay vessel phantoms, keeping the forbidden region 400 [Formula: see text]m outside and 400 [Formula: see text]m inside the vessel. Micron's tip entered the forbidden region with a median penetration of just 8.84 [Formula: see text]m and 9.63 [Formula: see text]m, compared to 148.74 [Formula: see text]m and 117.17 [Formula: see text]m without our strategy, for the former and latter trials, respectively.
Real-time control of Micron was achieved at 33.3 fps. We achieved improvements in real-time segmentation of brain vasculature from intra-operative images and showed that our approach works even on non-stationary vessel phantoms. The results suggest that by enabling precise, real-time control, we are one step closer to using Micron in real neurosurgical procedures.
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Details
- Title
- Real-time vessel segmentation and reconstruction for virtual fixtures for an active handheld microneurosurgical instrument
- Creators
- Aravind Venugopal - Birla Institute of Technology and Science, PilaniSara Moccia - Scuola Superiore Sant'AnnaSimone Foti - University College LondonArpita Routray - Carnegie Mellon UniversityRobert A MacLachlan - Carnegie Mellon UniversityAlessandro Perin - Fondazione IRCCS Istituto Neurologico Carlo BestaLeonardo S Mattos - Italian Institute of TechnologyAlexander K Yu - Allegheny Health NetworkJody Leonardo - Allegheny Health NetworkElena De Momi - Politecnico di MilanoCameron N Riviere - Carnegie Mellon University
- Publication Details
- International journal for computer assisted radiology and surgery, v 17(6), pp 1069-1077
- Grant note
- R01EB024564 / NIH HHS R01EB000526 / NIH HHS R01 EB024564 / NIBIB NIH HHS R01 EB000526 / NIBIB NIH HHS
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Surgery; Neurosurgery
- Web of Science ID
- WOS:000769839100004
- Scopus ID
- 2-s2.0-85126352054
- Other Identifier
- 991022059917004721
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- Collaboration types
- Domestic collaboration
- International collaboration
- Web of Science research areas
- Engineering, Biomedical
- Radiology, Nuclear Medicine & Medical Imaging
- Surgery