Prof. Kevin L. L. Schey Profile

Prof. Kevin L. L. Schey

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SPIE Journal Paper | 14 November 2024 Open Access

Super-resolution multi-contrast unbiased eye atlases with deep probabilistic refinement

Ho Hin Lee, Adam Saunders, Michael Kim, Samuel Remedios, Lucas Remedios, Yucheng Tang, Qi Yang, Xin Yu, Shunxing Bao, Chloe Cho, Louise Mawn, Tonia Rex, Kevin Schey, Blake Dewey, Jeffery Spraggins, Jerry Prince, Yuankai Huo, Bennett Landman

JMI, Vol. 11, Issue 06, 064004, (November 2024) https://doi.org/10.1117/12.10.1117/1.JMI.11.6.064004

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PurposeEye morphology varies significantly across the population, especially for the orbit and optic nerve. These variations limit the feasibility and robustness of generalizing population-wise features of eye organs to an unbiased spatial reference.ApproachTo tackle these limitations, we propose a process for creating high-resolution unbiased eye atlases. First, to restore spatial details from scans with a low through-plane resolution compared with a high in-plane resolution, we apply a deep learning-based super-resolution algorithm. Then, we generate an initial unbiased reference with an iterative metric-based registration using a small portion of subject scans. We register the remaining scans to this template and refine the template using an unsupervised deep probabilistic approach that generates a more expansive deformation field to enhance the organ boundary alignment. We demonstrate this framework using magnetic resonance images across four different tissue contrasts, generating four atlases in separate spatial alignments.ResultsWhen refining the template with sufficient subjects, we find a significant improvement using the Wilcoxon signed-rank test in the average Dice score across four labeled regions compared with a standard registration framework consisting of rigid, affine, and deformable transformations. These results highlight the effective alignment of eye organs and boundaries using our proposed process.ConclusionsBy combining super-resolution preprocessing and deep probabilistic models, we address the challenge of generating an eye atlas to serve as a standardized reference across a largely variable population.

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Proceedings Article | 3 April 2023 Poster + Paper

Unsupervised registration refinement for generating unbiased eye atlas

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

Proceedings Volume 12464, 1246422 (2023) https://doi.org/10.1117/12.2653753

KEYWORDS: Eye, Image registration, Deformation, Anatomy, Head, Computed tomography, Medical imaging

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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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