Official websites use .gov

A .gov website belongs to an official government organization in the United States.

Secure .gov websites use HTTPS

A lock ( 🔒 ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites.

Need Help

AI-empowered 3D Computer Vision and Image-Omics Integration for Digital Kidney Histopathology

Award Number: R01DK135597
ORGANIZATION: NATIONAL INSTITUTE OF DIABETES & DIGESTIVE & KIDNEY DISEASES
OPDIV: NIH
AWARD CLASS: DISCRETIONARY
AWARD ACTIVITY TYPE: SCIENTIFIC/HEALTH RESEARCH (INCLUDES SURVEYS)
PERIOD OF PERFORMANCE START DATE: 03/15/2023
PERIOD OF PERFORMANCE END DATE: 12/31/2027

Group Awards By:

View Award Description

AI-empowered 3D Computer Vision and Image-Omics Integration for Digital Kidney Histopathology - PROJECT SUMMARY Our overarching goal is to enable the AI-empowered 3D histopathological interpretation on routine digitized renal tissue biopsies, so as to (1) allow renal pathologists to perform a reproducible 3D phenotyping on serial 2D whole slide images (WSI), (2) advance the characterization of kidney-allograft rejection phenotypes on kidney transplant patients with cutting-edge 3D computer vision, and (3) equip clinical scientists with an advanced 3D spatial transcriptomics analytics tool to investigate the anatomical-molecular associated causes of chronic kidney disease (CKD). Our novel 3D histopathological interpretation, with 3D computer vision (Map3D toolkit) and 3D spatial transcriptomics, will open a new door for performing reproducible clinical phenotyping (Pheno3D toolkit), identifying and validating new 3D imaging and molecular biomarkers (GPS3D toolkit), and ultimately advancing the patient care with personalized diagnosis and prognosis options for a wide range of CKD. Despite more than 25 years of exploitation of digital pathology, the presence, significance and characteristics of 3D contextual information in renal histopathological assessment have been largely overlooked. The current 2D interpretation on renal histopathology is error-prone and less reproducible due to the heterogeneity of tissue morphologies (e.g., glomeruli, tubules, vessels) across 3D serial sections. For example, our previous study on segmental glomerulosclerosis (GS) in patients with nephrotic syndrome and idiopathic FSGS, the percent of GS increased from 31.5 +/- 6.8% to 48.0 +/- 6.6% (P < 0.025) in adults by replacing a 2D single section analysis with 3D serial section analysis. Moreover, 2D based phenotyping can also hinder the discovery of new biomarkers via state-of-the-art spatial transcriptomic techniques. As an example, a glomerulus with focal segmental glomerulosclerosis (FSGS) can have a normal appearance on a specific 2D section, which might lead to an opposite molecular finding using 2D spatial transcriptomics The core tenant of this proposal is NOT developing a new 3D imaging modality, but rather, to develop technologies that enable reproducible 3D characterization on routine 2D renal histopathological biopsies (with trivial added cost), so as to advance the care of future patients with renal diseases. To this end, we will: Aim 1. Develop novel 3D computer vision tools (Map3D) to facilitate renal pathologists in modeling, quantifying, and visualizing 3D renal histopathological tissues from routine 2D digital histopathology. Impact: Allow renal pathologists to perform a reproducible 3D phenotyping on serial 2D whole slide images (WSI). Aim 2. Develop 3D phenotyping tools (Pheno3D) to advance the characterization of kidney-allograft rejection for kidney transplant patients via 3D computer vision and self-supervised deep learning. Impact: Advance the characterization of kidney-allograft rejection phenotypes for kidney transplant patients. Aim 3. Develop 3D computer vision algorithms for 2D and 3D spatial transcriptomics (GPS3D toolkit). Impact: Equip clinical scientists an 3D spatial transcriptomics analytics tool to investigate image-omics interaction.


Issue Date FY	Funding FY	Legal Entity Name	Legal Entity Address	Legal Entity City	Legal Entity State	Legal Entity Zip Code	Legal Entity COUNTY	Legal Entity COUNTRY	Assistance Listing	Award Code	Budget Year	Action Date	Action Type	Action Amount

Issue Date FY: 2026 ( Subtotal = $439,044 )
2026	2026	VANDERBILT UNIVERSITY	110 21ST AVE S	NASHVILLE	TN	37203	DAVIDSON	USA	Diabetes, Digestive, and Kidney Diseases Extramural Research	000	4	12/18/2025	NON-COMPETING CONTINUATION	$439,044
														Subtotal = $439,044

Issue Date FY: 2025 ( Subtotal = $516,487 )
2025	2025	VANDERBILT UNIVERSITY	110 21ST AVE S	NASHVILLE	TN	37203	DAVIDSON	USA	Diabetes, Digestive, and Kidney Diseases Extramural Research	000	3	12/23/2024	NON-COMPETING CONTINUATION	$516,487
														Subtotal = $516,487

Issue Date FY: 2024 ( Subtotal = $559,369 )
2024	2024	VANDERBILT UNIVERSITY	110 21ST AVE S	NASHVILLE	TN	37203	DAVIDSON	USA	Diabetes, Digestive, and Kidney Diseases Extramural Research	001	2	5/3/2024	NON-COMPETING CONTINUATION	$34,962
2024	2024	VANDERBILT UNIVERSITY	110 21ST AVE S	NASHVILLE	TN	37203	DAVIDSON	USA	Diabetes, Digestive, and Kidney Diseases Extramural Research	000	2	12/7/2023	NON-COMPETING CONTINUATION	$524,407
														Subtotal = $559,369

Issue Date FY: 2023 ( Subtotal = $614,900 )
2023	2023	VANDERBILT UNIVERSITY	110 21ST AVE S	NASHVILLE	TN	37203	DAVIDSON	USA	Diabetes, Digestive, and Kidney Diseases Extramural Research	000	1	3/3/2023	NEW	$614,900
														Subtotal = $614,900

Grand Total All Awards = $2,129,800

Top

All Categories

About

Search

Reports

Data Submission

Award Information

AI-empowered 3D Computer Vision and Image-Omics Integration for Digital Kidney Histopathology

Award Number: R01DK135597

ORGANIZATION: NATIONAL INSTITUTE OF DIABETES & DIGESTIVE & KIDNEY DISEASES

OPDIV: NIH

AWARD CLASS: DISCRETIONARY

AWARD ACTIVITY TYPE: SCIENTIFIC/HEALTH RESEARCH (INCLUDES SURVEYS)

PERIOD OF PERFORMANCE START DATE: 03/15/2023

PERIOD OF PERFORMANCE END DATE: 12/31/2027

Federal Websites

Department of Health & Human Services

HHS Operating Divisions

HHS Staff Divisions

Download A Document Viewer