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5. Distortion and instability compensation with deep learning for rotational scanning endoscopic optical coherence tomography

Author

Guiqiu, Liao, Oscar, Caravaca-Mora, Benoit, Rosa, Philippe, Zanne, Diego, Dall'Alba, Paolo, Fiorini, Michel, de Mathelin, Florent, Nageotte, Michalina J, Gora, Laboratoire des sciences de l'ingénieur, de l'informatique et de l'imagerie (ICube), École Nationale du Génie de l'Eau et de l'Environnement de Strasbourg (ENGEES)-Université de Strasbourg (UNISTRA)-Institut National des Sciences Appliquées - Strasbourg (INSA Strasbourg), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Les Hôpitaux Universitaires de Strasbourg (HUS)-Centre National de la Recherche Scientifique (CNRS)-Matériaux et Nanosciences Grand-Est (MNGE), Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut de Chimie du CNRS (INC)-Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut de Chimie du CNRS (INC)-Centre National de la Recherche Scientifique (CNRS)-Réseau nanophotonique et optique, Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA)-Centre National de la Recherche Scientifique (CNRS), Università degli studi di Verona = University of Verona (UNIVR), and univOAK, Archive ouverte

Subjects
Video stabilization, Optical coherence tomography, Neural Networks, Radiological and Ultrasound Technology, [SDV.IB.IMA]Life Sciences [q-bio]/Bioengineering/Imaging, Image correction, [INFO.INFO-IM] Computer Science [cs]/Medical Imaging, Endoscopic catheter, Convolutional neural network, Health Informatics, Computer Graphics and Computer-Aided Design, Computer, [SDV.IB.IMA] Life Sciences [q-bio]/Bioengineering/Imaging, Deep Learning, Optical Coherence, 2000 MSC: 41A05, 41A10, 65D05, 65D17, [INFO.INFO-IM]Computer Science [cs]/Medical Imaging, Humans, Radiology, Nuclear Medicine and imaging, Neural Networks, Computer, Computer Vision and Pattern Recognition, Artifacts, Tomography, Algorithms, Tomography, Optical Coherence
Abstract

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.

Published
2022
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