i3PosNet: instrument pose estimation from X-ray in temporal bone surgery.
Int J Comput Assist Radiol Surg
; 15(7): 1137-1145, 2020 Jul.
Article
en En
| MEDLINE
| ID: mdl-32440956
ABSTRACT
PURPOSE:
Accurate estimation of the position and orientation (pose) of surgical instruments is crucial for delicate minimally invasive temporal bone surgery. Current techniques lack in accuracy and/or line-of-sight constraints (conventional tracking systems) or expose the patient to prohibitive ionizing radiation (intra-operative CT). A possible solution is to capture the instrument with a c-arm at irregular intervals and recover the pose from the image.METHODS:
i3PosNet infers the position and orientation of instruments from images using a pose estimation network. Said framework considers localized patches and outputs pseudo-landmarks. The pose is reconstructed from pseudo-landmarks by geometric considerations.RESULTS:
We show i3PosNet reaches errors [Formula see text] mm. It outperforms conventional image registration-based approaches reducing average and maximum errors by at least two thirds. i3PosNet trained on synthetic images generalizes to real X-rays without any further adaptation.CONCLUSION:
The translation of deep learning-based methods to surgical applications is difficult, because large representative datasets for training and testing are not available. This work empirically shows sub-millimeter pose estimation trained solely based on synthetic training data.Palabras clave
Texto completo:
1
Colección:
01-internacional
Banco de datos:
MEDLINE
Asunto principal:
Procedimientos Quirúrgicos Otológicos
/
Hueso Temporal
/
Cirugía Asistida por Computador
Tipo de estudio:
Diagnostic_studies
Límite:
Humans
Idioma:
En
Revista:
Int J Comput Assist Radiol Surg
Asunto de la revista:
RADIOLOGIA
Año:
2020
Tipo del documento:
Article
País de afiliación:
Alemania