Work

AutoGlom: software tool for segmentation and analysis of magnetic resonance images of the kidney

Year: 2025

Type: article

Abstract: We present AutoGlom, an open-source software for quantitative kidney MRI analysis. AutoGlom integrates deep learning-based glomerular segmentation, parameter tuning, and visualization within a user-friendly interface. It enables high-throughput analysis using a 3-D-printed holder for simultaneous imaging of multiple kidneys and introduces a new image quality metric, glomerular contrast, to improve reliability. AutoGlom provides standardized, reproducible workflows for glomerular quantification, bridging preclinical and translational kidney imaging and enabling future physiological discoveries.

Source: American Journal of Physiology-Renal Physiology

DOI: 10.1152/ajprenal.00325.2025

Authors: Teng Li, Adam Cochran, Yanzhe Xu, Jennifer R. Charlton, Kevin M. Bennett, Sage M. Timberline, Rachel K. Dailey, Syeda Y. Jannath, Edwin J. Baldelomar, Matthew R. Hoch, Teresa Wu

Institutions: Arizona State University, Amazon (United States), Meta (United States), University of Virginia Children's Hospital, Washington University in St. Louis, University of Virginia

Language: English

Topic: Medical Image Segmentation Techniques

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: hybrid

Funders: National Institutes of Health, National Institute of Diabetes and Digestive and Kidney Diseases

Awards: Investigating the role of nephron mass in the progression of CKD using MRI, G1296320462, G2274654633, G4110880326, Small-Animal 9.4T MRI for Biomedical Research, Regulation of Cell Fate during Kidney Development and Disease

OpenAlex ID: W4416428255

FWCI: 0.724

Cites: 34

Cited by: 2

Related to: