# Artificial Intelligence-Based Approaches for Renal Structure Characterization in Computed Tomography Images

> **NIH NIH R03** · MAYO CLINIC ROCHESTER · 2020 · $113,350

## Abstract

ABSTRACT
The goal of this R03 Small Grant Program for NIDDK is to provide additional funding for Dr. Kline to expand
upon his work on his K award and apply his expertise to new image acquisitions and problems related to renal
imaging. Dr. Kline’s work has piqued the interest of many internal and external investigators and has led to
recent collaborations with Drs. Rule, Denic, and Kim. Together with Dr. Erickson, this new research team has
prepared this R03 proposal which takes advantage of the unique expertise of each team member. The focus of
this proposal is to bridge the gap between microscopic observations and those assessable non-invasively by
radiological imaging. To do this, we have established a unique dataset of renal CT imaging data and
corresponding biopsy measured nephron densities. We have also generated a large database of gold-standard
segmentation data of kidneys, cortical regions, and medullary pyramids. Using this existing data, we propose
to: (i) develop tools for segmentation of kidneys, segmentation of individual medullary pyramids, and imputing
missing parts of the kidneys outside of the imaged field-of-view in the CT image, and (ii) to establish imaging
biomarkers of early CKD, and correlate macroscopic imaging findings to underlying microscopic structure. This
research will be facilitated by Mayo Clinic’s outstanding clinical and research environment dedicated to
improving patient care, as well as the Aging Kidney Anatomy Study (PI: Rule), which led to the generation of
this unique and well characterized dataset. Dr. Kline’s background in imaging technologies and image
processing makes him particularly well suited to perform this research. In addition to the above aims, near the
end of this research project Dr. Kline will submit a highly competitive R01 application expanding upon the
findings from this research proposal. This proposal will lead to vast improvements to current analysis
workflows, as well as an improved understanding of the prognostic power of renal imaging biomarkers.
Obtaining this R03 Award will greatly facilitate Dr. Kline’s transition into a prosperous independent researcher
focused on developing novel imaging technologies and image analysis techniques for abdominal organ
pathologies.

## Key facts

- **NIH application ID:** 10040835
- **Project number:** 1R03DK125632-01
- **Recipient organization:** MAYO CLINIC ROCHESTER
- **Principal Investigator:** Timothy Lee Kline
- **Activity code:** R03 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $113,350
- **Award type:** 1
- **Project period:** 2020-08-01 → 2022-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10040835

## Citation

> US National Institutes of Health, RePORTER application 10040835, Artificial Intelligence-Based Approaches for Renal Structure Characterization in Computed Tomography Images (1R03DK125632-01). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10040835. Licensed CC0.

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