Distortion and instability compensation with deep learning for rotational scanning endoscopic optical coherence tomography.
Fiche publication
Date publication
janvier 2022
Journal
Medical image analysis
Auteurs
Membres identifiés du Cancéropôle Est :
Pr DE MATHELIN Michel
Tous les auteurs :
Liao G, Caravaca-Mora O, Rosa B, Zanne P, Dall'Alba D, Fiorini P, de Mathelin M, Nageotte F, Gora MJ
Lien Pubmed
Résumé
Optical Coherence Tomography (OCT) is increasingly used in endoluminal procedures since it provides high-speed and high resolution imaging. Distortion and instability of images obtained with a proximal scanning endoscopic OCT system are significant due to the motor rotation irregularity, the friction between the rotating probe and outer sheath and synchronization issues. On-line compensation of artefacts is essential to ensure image quality suitable for real-time assistance during diagnosis or minimally invasive treatment. In this paper, we propose a new online correction method to tackle both B-scan distortion, video stream shaking and drift problem of endoscopic OCT linked to A-line level image shifting. The proposed computational approach for OCT scanning video correction integrates a Convolutional Neural Network (CNN) to improve the estimation of azimuthal shifting of each A-line. To suppress the accumulative error of integral estimation we also introduce another CNN branch to estimate a dynamic overall orientation angle. We train the network with semi-synthetic OCT videos by intentionally adding rotational distortion into real OCT scanning images. The results show that networks trained on this semi-synthetic data generalize to stabilize real OCT videos, and the algorithm efficacy is demonstrated on both ex vivo and in vivo data, where strong scanning artifacts are successfully corrected.
Mots clés
Convolutional neural network, Endoscopic catheter, Image correction, Optical coherence tomography, Video stabilization
Référence
Med Image Anal. 2022 Jan 22;77:102355