Surgical Navigation System for Transsphenoidal Pituitary Surgery Applying U-Net-Based Automatic Segmentation and Bendable Devices
Source abstract
Abstract
Conventional navigation systems used in transsphenoidal pituitary surgery have limitations that may lead to organ damage, including long image registration time, absence of alarms when approaching vital organs and lack of 3-D model information. To resolve the problems of conventional navigation systems, this study proposes a U-Net-based, automatic segmentation algorithm for optical nerves and internal carotid arteries, by training patient computed tomography angiography images. The authors have also developed a bendable endoscope and surgical tool to eliminate blind regions that occur when using straight, rigid, conventional endoscopes and surgical tools during transsphenoidal pituitary surgery. In this study, the effectiveness of a U-Net-based navigation system integrated with bendable surgical tools and a bendable endoscope has been demonstrated through phantom-based experiments. In order to measure the U-net performance, the Jaccard similarity, recall and precision were calculated. In addition, the fiducial and target registration errors of the navigation system and the accuracy of the alarm warning functions were measured in the phantom-based environment.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Pituitary Gland Disorders and Treatments · Surgical Simulation and Training · Augmented Reality Applications
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Electrical and Electronic Engineering, Hanyang University, Gyeonggi-do 15588, Korea
Center for Intelligent & Interactive Robotics, Korea Institute of Science and Technology, Seoul 02792, Korea
Department of Robotics Engineering, Daegu Gyeongbuk Institute of Science and Technology, Daegu 42988, Korea
Department of Neurosurgery, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul 02792, Korea
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