TY  - JOUR
AU  - Juan Borrego-Carazo
AU  - Carles Sanchez
AU  - David Castells
AU  - Jordi Carrabina
AU  - Debora Gil
PY  - 2023//
TI  - BronchoPose: an analysis of data and model configuration for vision-based bronchoscopy pose estimation
T2  - CMPB
JO  - Computer Methods and Programs in Biomedicine
SP  - 107241
VL  - 228
PB  - Elsevier
KW  - Videobronchoscopy guiding
KW  - Deep learning
KW  - Architecture optimization
KW  - Datasets
KW  - Standardized evaluation framework
KW  - Pose estimation
N2  - Vision-based bronchoscopy (VB) models require the registration of the virtual lung model with the frames from the video bronchoscopy to provide effective guidance during the biopsy. The registration can be achieved by either tracking the position and orientation of the bronchoscopy camera or by calibrating its deviation from the pose (position and orientation) simulated in the virtual lung model. Recent advances in neural networks and temporal image processing have provided new opportunities for guided bronchoscopy. However, such progress has been hindered by the lack of comparative experimental conditions.In the present paper, we share a novel synthetic dataset allowing for a fair comparison of methods. Moreover, this paper investigates several neural network architectures for the learning of temporal information at different levels of subject personalization. In order to improve orientation measurement, we also present a standardized comparison framework and a novel metric for camera orientation learning. Results on the dataset show that the proposed metric and architectures, as well as the standardized conditions, provide notable improvements to current state-of-the-art camera pose estimation in video bronchoscopy.
L1  - http://refbase.cvc.uab.es/files/BSC2023.pdf
UR  - http://dx.doi.org/10.1016/j.cmpb.2022.107241
N1  - IAM;
ID  - Juan Borrego-Carazo2023
ER  -