ArticleProceedings of SPIE--the International Society for Optical Engineering
Supervised Deep Generation of High-Resolution Arterial Phase Computed Tomography Kidney Substructure Atlas.
Article in Proceedings of SPIE--the International Society for Optical Engineering. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Multi-contrast computed tomography atlas of healthy pancreas with dense displacement sampling registration.Journal of medical imaging (Bellingham, Wash.) · 2025Article
- Super-resolution multi-contrast unbiased eye atlases with deep probabilistic refinement.Journal of medical imaging (Bellingham, Wash.) · 2024Article
- Characterizing Low-cost Registration for Photographic Images to Computed Tomography.Proceedings of SPIE--the International Society for Optical Engineering · 2024Article
- Article
- Unsupervised Registration Refinement for Generating Unbiased Eye Atlas.Proceedings of SPIE--the International Society for Optical Engineering · 2023Article
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Authors and funding
12 authors.
Funding
Abstract
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.
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