Detección por deep learning de puntos de riesgo en la trayectoria de un asistente robotizado en cirugías endonasales transesfenoidales

  1. Fontúrbel, Carlos 1
  2. Ayuso-Lera, Adrián 1
  3. Fuente, Eusebio de la 1
  4. Fraile, Juan Carlos 1
  5. Pérez Turiel, Javier 1
  1. 1 Universidad de Valladolid
    info

    Universidad de Valladolid

    Valladolid, España

    ROR https://ror.org/01fvbaw18

Book:
XLIII Jornadas de Automática: libro de actas: 7, 8 y 9 de septiembre de 2022, Logroño (La Rioja)
  1. Carlos Balaguer Bernaldo de Quirós (coord.)
  2. José Manuel Andújar Márquez (coord.)
  3. Ramon Costa Castelló (coord.)
  4. Carlos Ocampo Martínez (coord.)
  5. Jesús Fernández Lozano (coord.)
  6. Matilde Santos Peñas (coord.)
  7. José Enrique Simó Ten (coord.)
  8. Montserrat Gil Martínez (coord.)
  9. Jose Luis Calvo Rolle (coord.)
  10. Raúl Marín Prades (coord.)
  11. Eduardo Rocón de Lima (coord.)
  12. Elisabet Estévez Estévez (coord.)
  13. Pedro Jesús Cabrera Santana (coord.)
  14. David Muñoz de la Peña Sequedo (coord.)
  15. José Luis Guzmán Sánchez (coord.)
  16. José Luis Pitarch Pérez (coord.)
  17. Oscar Reinoso García (coord.)
  18. Oscar Déniz Suárez (coord.)
  19. Emilio Jiménez Macías (coord.)
  20. 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

DIALNET GOOGLE SCHOLAR lock_openRUC editor

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.

Data source: Dialnet