Mr. Jeffery M. Spraggins Profile

Jeffery M. Spraggins

at Vanderbilt Univ

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Author

Publications (5)

Proceedings Article | 2 April 2024 Poster + Paper

Characterizing low-cost registration for photographic images to computed tomography

Michael Kim, Ho Hin Lee, Karthik Ramadass, Chenyu Gao, Katherine Van Schaik, Eric Tkaczyk, Jeffrey Spraggins, Daniel Moyer, Bennett Landman

Proceedings Volume 12930, 1293025 (2024) https://doi.org/10.1117/12.3005578

KEYWORDS: Photogrammetry, Computed tomography, Cameras, Video, Natural surfaces, Photography, Image registration, Tissues, 3D image processing

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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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Proceedings Article | 4 April 2022 Poster + Paper

Supervised deep generation of high-resolution arterial phase computed tomography kidney substructure atlas

Ho Hin Lee, Yucheng Tang, Shunxing Bao, Qi Yang, Xin Yu, Agnes Fogo, Raymond Harris, Mark de Caestecker, Jeffery Spraggins, Mattias Heinrich, Yuankai Huo, Bennett Landman

Proceedings Volume 12032, 120322S (2022) https://doi.org/10.1117/12.2608290

KEYWORDS: Kidney, Image registration, Computed tomography, Image segmentation, Diffusion, 3D modeling, Visualization, Machine learning, Spatial learning

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The Human BioMolecular Atlas Program (HuBMAP) provides an opportunity to contextualize findings across cellular to organ systems levels. Constructing an atlas target is the primary endpoint for generalizing anatomical information across scales and populations. An initial target of HuBMAP is the kidney organ and arterial phase contrast-enhanced computed tomography (CT) provides distinctive appearance and anatomical context on the internal substructure of kidney organs such as renal context, medulla, and pelvicalyceal system. With the confounding effects of demographics and morphological characteristics of the kidney across large-scale imaging surveys, substantial variation is demonstrated with the internal substructure morphometry and the intensity contrast due to the variance of imaging protocols. Such variability increases the level of difficulty to localize the anatomical features of the kidney substructure in a well-defined spatial reference for clinical analysis. In order to stabilize the localization of kidney substructures in the context of this variability, we propose a high-resolution CT kidney substructure atlas template. Briefly, we introduce a deep learning preprocessing technique to extract the volumetric interest of the abdominal regions and further perform a deep supervised registration pipeline to stably adapt the anatomical context of the kidney internal substructure. To generate and evaluate the atlas template, arterial phase CT scans of 500 control subjects are de-identified and registered to the atlas template with a complete end-to-end pipeline. With stable registration to the abdominal wall and kidney organs, the internal substructure of both left and right kidneys are substantially localized in the high-resolution atlas space. The atlas average template successfully demonstrated the contextual details of the internal structure and was applicable to generalize the morphological variation of internal substructure across patients.

Proceedings Article | 15 February 2021 Presentation + Paper

Renal cortex, medulla and pelvicaliceal system segmentation on arterial phase CT images with random patch-based networks

Yucheng Tang, Riqiang Gao, Ho Hin Lee, Zhoubing Xu, Brent Savoie, Shunxing Bao, Yuankai Huo, Agnes Fogo, Raymond Harris, Mark de Caestecker, Jeffery Spraggins, Bennett Landman

Proceedings Volume 11596, 115961D (2021) https://doi.org/10.1117/12.2581101

KEYWORDS: Image segmentation, Computed tomography, Kidney, Neural networks

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Proceedings Article | 15 February 2021 Presentation + Paper

Construction of a multi-phase contrast computed tomography kidney atlas

Ho Hin Lee, Yucheng Tang, Kaiwen Xu, Shunxing Bao, Agnes Fogo, Raymond Harris, Mark de Caestecker, Mattias Heinrich, Jeffery Spraggins, Yuankai Huo, Bennett Landman

Proceedings Volume 11596, 115961T (2021) https://doi.org/10.1117/12.2580561

KEYWORDS: Kidney, Computed tomography, Information visualization, Visualization, Tissues, Associative arrays, Abdomen

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