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    Biomedical Imaging and Instrumentation

    (D-141) Reconstructing the entire anterior segment through montaging visible-light optical coherence tomography volumes from distinct poses

    Saturday, October 14, 2023
    9:30 AM – 10:30 AM PDT
    Location: Exhibit Hall - Row D - Poster # 141

      Presenting Author(s)

    • DK

      Daniel S. Kim (he/him/his)

      Student
      Northwestern University
      Aurora, Illinois, United States

      • Co-Author(s)

    • RF

      Raymond Fang

      M.D./Ph.D. student
      Northwestern University, United States

      • Primary Investigator(s)

    • HZ

      Hao Zhang

      Principal Investigator
      Northwestern University, United States

    Introduction:: Owing to its high axial resolution, optical coherence tomography (OCT) has the capacity to reconstruct 3-dimensional structures with micron precision. However, due to tissue scattering, small field of view per scan, and limited depth of focus, it is challenging to reconstruct large, complex volumetric objects that do not fit within one OCT scan. To address this problem, we developed novel montaging schemes to combine OCT volumes acquired from different angles and positions. We demonstrate the effectiveness of this method by measuring the aqueous volume of the anterior and posterior chambers of the eye.

    Materials and Methods:: We developed a robotic visible-light OCT system (vis-OCT) and acquired volumes of the full 360 degrees of a 10-month-old B6 albino mouse. Vis-OCT is a promising new technology with an axial resolution of around 1.3 µm in tissue, providing the details needed for accurate segmentation of regions within the mouse eye. We acquired images of the anterior and posterior chamber around the full 360 degrees of the anterior segment of the mouse eye. Following data acquisition, we obtained the boundary of the mouse eye by using the Facebook Segment Anything Model applied through Biodock to segment the anterior and posterior chambers of the mouse eye in each cross-sectional B-scan. Once the segmentation was obtained, they were rendered into 3-dimensional volumes by representing each of the cross-sections across the depth of the scan. Control points, which were the branches of blood vessels in the anterior, were used to match boundary points between volumes acquired at different poses. Using the relative positions between montaged poses, we reconstructed the anterior and posterior chambers of the mouse eye.

    Results, Conclusions, and Discussions:: We reconstructed the entire anterior segment of the eye. Figure 1 demonstrates the representation of the anterior segment and the corresponding rendered volume of the anterior chamber. Similarly, Figure 2 shows the results of the segmentation and montaging of the posterior chamber. Figures 1a/2a, 1b/2b, and 1c/2c display the cross-sectional areas of the segmented anterior/posterior chambers across the xy, xz, and yz planes respectively. Figures 1d and 2d provide an isometric view of the rendered 3-dimensional volume of the anterior and posterior chambers respectively. The figures demonstrate that the montaged structures between different volumetric scans aligned well. Using branches of blood vessels as control points allows the volumes across different B-scans to be matched and montaged properly. Overall, we measured the anterior and posterior chamber volume for this mouse eye to be 1.612 µL and 1.861 µL respectively. The continuity of the aqueous volumes around the full 360 degrees of the eye demonstrates that our method is effective in montaging volumes acquired by OCT. We reconstructed a method for montaging volumetric OCT images from different poses, with deviations in both angles and positions. The methods developed will assist investigators in reconstructing large OCT volumes with micron precision. This method allows for the generation of continuous and accurate 3D reconstructions of the aqueous volumes around the full 360 degrees of the eye.

    Acknowledgements (Optional): :

    This work was supported by NIH (R01EY029121, R01EY030501, R01EY033813).



    References (Optional): :

    Biodock, AI Software Platform. Biodock 2023.  Available from www.biodock.ai.


    Kirillov A, Mintun E, Ravi N, Mao H, Rolland C, Gustafson L, et al. Segment anything. arXiv; 2023. Available from: https://arxiv.org/abs/2304.02643