TY - JOUR

T1 - Volumetric 3D stitching of optical coherence tomography volumes

AU - Laves, Max Heinrich

AU - Kahrs, Lüder A.

AU - Ortmaier, Tobias

N1 - Funding information: Research funding: This research has received funding from the European Union as being part of the ERFE OPhonLas project. Conflict of interest: The authors state no conflict of interest. Informed consent: Informed consent is not applicable. Ethical approval: For this kind of study ethical approval is not required.

PY - 2018/9/22

Y1 - 2018/9/22

N2 - Optical coherence tomography (OCT) is a non-invasive medical imaging modality, which provides highresolution transectional images of biological tissue. However, its potential is limited due to a relatively small field of view. To overcome this drawback, we describe a scheme for fully automated stitching of multiple 3D OCT volumes for panoramic imaging. The voxel displacements between two adjacent images are calculated by extending the Lucas-Kanade optical flow algorithm to dense volumetric images. A RANSAC robust estimator is used to obtain rigid transformations out of the resulting flow vectors. The images are transformed into the same coordinate frame and overlapping areas are blended. The accuracy of the proposed stitching scheme is evaluated on two datasets of 7 and 4 OCT volumes, respectively. By placing the specimens on a high-accuracy motorized translational stage, ground truth transformations are available. This results in a mean translational error between two adjacent volumes of 16.6 ± 0.8 μm (2.8 ± 0.13 voxels). To the author's knowledge, this is the first reported stitching of multiple 3D OCT volumes by using dense voxel information in the registration process. The achieved results are sufficient for providing high accuracy OCT panoramic images. Combined with a recently available high-speed 4D OCT, our method enables interactive stitching of hand-guided acquisitions.

AB - Optical coherence tomography (OCT) is a non-invasive medical imaging modality, which provides highresolution transectional images of biological tissue. However, its potential is limited due to a relatively small field of view. To overcome this drawback, we describe a scheme for fully automated stitching of multiple 3D OCT volumes for panoramic imaging. The voxel displacements between two adjacent images are calculated by extending the Lucas-Kanade optical flow algorithm to dense volumetric images. A RANSAC robust estimator is used to obtain rigid transformations out of the resulting flow vectors. The images are transformed into the same coordinate frame and overlapping areas are blended. The accuracy of the proposed stitching scheme is evaluated on two datasets of 7 and 4 OCT volumes, respectively. By placing the specimens on a high-accuracy motorized translational stage, ground truth transformations are available. This results in a mean translational error between two adjacent volumes of 16.6 ± 0.8 μm (2.8 ± 0.13 voxels). To the author's knowledge, this is the first reported stitching of multiple 3D OCT volumes by using dense voxel information in the registration process. The achieved results are sufficient for providing high accuracy OCT panoramic images. Combined with a recently available high-speed 4D OCT, our method enables interactive stitching of hand-guided acquisitions.

KW - Image processing

KW - Medical imaging

KW - Mosaicing

KW - Optical flow

KW - Panoramic imaging

KW - Registration

UR - http://www.scopus.com/inward/record.url?scp=85059770222&partnerID=8YFLogxK

U2 - 10.1515/cdbme-2018-0079

DO - 10.1515/cdbme-2018-0079

M3 - Article

AN - SCOPUS:85059770222

VL - 4

SP - 327

EP - 330

JO - Current Directions in Biomedical Engineering

JF - Current Directions in Biomedical Engineering

IS - 1

ER -