Detección por deep learning de puntos de riesgo en la trayectoria de un asistente robotizado en cirugías endonasales transesfenoidales
- Fontúrbel, Carlos 1
- Ayuso-Lera, Adrián 1
- Fuente, Eusebio de la 1
- Fraile, Juan Carlos 1
- Pérez Turiel, Javier 1
-
1
Universidad de Valladolid
info
- Carlos Balaguer Bernaldo de Quirós (coord.)
- José Manuel Andújar Márquez (coord.)
- Ramon Costa Castelló (coord.)
- Carlos Ocampo Martínez (coord.)
- Jesús Fernández Lozano (coord.)
- Matilde Santos Peñas (coord.)
- José Enrique Simó Ten (coord.)
- Montserrat Gil Martínez (coord.)
- Jose Luis Calvo Rolle (coord.)
- Raúl Marín Prades (coord.)
- Eduardo Rocón de Lima (coord.)
- Elisabet Estévez Estévez (coord.)
- Pedro Jesús Cabrera Santana (coord.)
- David Muñoz de la Peña Sequedo (coord.)
- José Luis Guzmán Sánchez (coord.)
- José Luis Pitarch Pérez (coord.)
- Oscar Reinoso García (coord.)
- Oscar Déniz Suárez (coord.)
- Emilio Jiménez Macías (coord.)
- Vanesa Loureiro Vázquez (coord.)
Publisher: Servizo de Publicacións ; Universidade da Coruña
ISBN: 978-84-9749-841-8
Year of publication: 2022
Pages: 993-1000
Congress: Jornadas de Automática (43. 2022. Logroño)
Type: Conference paper
Sustainable development goals
Abstract
Transsphenoidal endoscopic endonasal surgery is a minimally invasive procedure currently used to remove pituitary tumours. Despite its benefits, current technology requires the participation of two surgeons who have to perform this operation with a very high degree of precision in a very tight and extremely delicate area. The automation of certain procedures during the intervention, such as the handling of the endoscope by a robotic assistant, could obviate the current need for a second surgeon, who performs this task. To achieve this, it is necessary to generate a reference trajectory for the robotic arm that would be the carrier of the endoscope. This paper proposes a method for generating the path manually with automatic risk verification of the trajectory by detecting the location of segmented critical anatomical structures using deep learning.