Unsupervised registration refinement for generating unbiased eye atlas

Ho Hin Lee; Yucheng Tang; Shunxing Bao; Qi Yang; Xin Yu; Kevin L. Schey; Jeffery M. Spraggins; Yuankai Huo; Bennett A. Landman

doi:10.1117/12.2653753

3 April 2023 Unsupervised registration refinement for generating unbiased eye atlas

Ho Hin Lee, Yucheng Tang, Shunxing Bao, Qi Yang, Xin Yu, Kevin L. Schey, Jeffery M. Spraggins, Yuankai Huo, Bennett A. Landman

Author Affiliations +

Proceedings Volume 12464, Medical Imaging 2023: Image Processing; 1246422 (2023) https://doi.org/10.1117/12.2653753
Event: SPIE Medical Imaging, 2023, San Diego, California, United States

Conference Poster

Abstract

With the confounding effects of demographics across large-scale imaging surveys, substantial variation is demonstrated with the volumetric structure of orbit and eye anthropometry. Such variability increases the level of difficulty to localize the anatomical features of the eye organs for populational analysis. To adapt the variability of eye organs with stable registration transfer, we propose an unbiased eye atlas template followed by a hierarchical coarse-to-fine approach to provide generalized eye organ context across populations. Furthermore, we retrieved volumetric scans from 1842 healthy patients for generating an eye atlas template with minimal biases. Briefly, we select 20 subject scans and use an iterative approach to generate an initial unbiased template. We then perform metric-based registration to the remaining samples with the unbiased template and generate coarse registered outputs. The coarse registered outputs are further leveraged to train a deep probabilistic network, which aims to refine the organ deformation in unsupervised setting. Computed tomography (CT) scans of 100 de-identified subjects are used to generate and evaluate the unbiased atlas template with the hierarchical pipeline. The refined registration shows the stable transfer of the eye organs, which were well-localized in the high-resolution (0.5 mm³) atlas space and demonstrated a significant improvement of 2.37% Dice for inverse label transfer performance. The subject-wise qualitative representations with surface rendering successfully demonstrate the transfer details of the organ context and showed the applicability of generalizing the morphological variation across patients

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Ho Hin Lee, Yucheng Tang, Shunxing Bao, Qi Yang, Xin Yu, Kevin L. Schey, Jeffery M. Spraggins, Yuankai Huo, and Bennett A. Landman "Unsupervised registration refinement for generating unbiased eye atlas", Proc. SPIE 12464, Medical Imaging 2023: Image Processing, 1246422 (3 April 2023); https://doi.org/10.1117/12.2653753

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