Evidence map›Paper›PMID 41323934›Full record

ArticleProceedings of SPIE--the International Society for Optical Engineering2025

GLAM: Glomeruli Segmentation for Human Pathological Lesions using Adapted Mouse Model.

Lining Yu, Mengmeng Yin, Ruining Deng, Quan Liu, Tianyuan Yao, Can Cui, Yitian Long, Yu Wang, Yaohong Wang, Shilin Zhao and 2 more

Abstract read
In one paragraph

Article in Proceedings of SPIE--the International Society for Optical Engineering, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors.

Lining YuDepartment of Computer Science, Vanderbilt University, Nashville, TN, USA.
Mengmeng YinDepartment of Pathology, Microbiology and Immunology, Vanderbilt University Medical Center, Nashville, TN, USA.
Ruining DengDepartment of Computer Science, Vanderbilt University, Nashville, TN, USA.
Quan LiuDepartment of Computer Science, Vanderbilt University, Nashville, TN, USA.
Tianyuan YaoDepartment of Computer Science, Vanderbilt University, Nashville, TN, USA.
Can CuiDepartment of Computer Science, Vanderbilt University, Nashville, TN, USA.
Yitian LongData Science Institute, Vanderbilt University, Nashville, TN, USA.
Yu WangDepartment of Pathology, Microbiology and Immunology, Vanderbilt University Medical Center, Nashville, TN, USA.
Yaohong WangDepartment of Anatomical Pathology, UT MD Anderson Cancer Center, TX, USA.
Shilin ZhaoDepartment of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, USA.
Haichun YangDepartment of Pathology, Microbiology and Immunology, Vanderbilt University Medical Center, Nashville, TN, USA.
Yuankai HuoDepartment of Computer Science, Vanderbilt University, Nashville, TN, USA.

Funding

Controlling Quality and Capturing Uncertainty in Advanced Diffusion Weighted MRIR01EB017230 · NIBIB · VANDERBILT UNIVERSITY · PI LANDMAN, BENNETT A. · 2015 to 2024
$5.1M
RESOLUTION OF GLOMERULOSCLEROSISR01DK056942 · NIDDK · VANDERBILT UNIVERSITY MEDICAL CENTER · PI FOGO, AGNES B. · 2001 to 2021
$4.9M
Complementary ex vivo multimodal optical imaging and in vivo Raman spectroscopy to understand tissue dynamicsR01EB033385 · NIBIB · VANDERBILT UNIVERSITY · PI MAHADEVAN-JANSEN, ANITA, REESE, JOHN JEFFREY · 2022 to 2025
$2.6M
AI-empowered 3D Computer Vision and Image-Omics Integration for Digital Kidney HistopathologyR01DK135597 · NIDDK · VANDERBILT UNIVERSITY · PI Yuankai Huo · 2023 to 2026
$2.1M
Dual-wavelength endoscopic Raman probe for eosinophilic esophagitisR01DK132338 · NIDDK · VANDERBILT UNIVERSITY · PI MAHADEVAN-JANSEN, ANITA · 2022 to 2025
$1.7M
fMRI physiological signatures of aging and Alzheimer's DiseaseRF1MH125931 · NIMH · VANDERBILT UNIVERSITY · PI CHANG, CATHERINE ELIZABETH · 2021 to 2021
$1.1M
NIBIB NIH HHS R01 EB017230NIBIB NIH HHS R01 EB033385NIDDK NIH HHS R01 DK056942NIDDK NIH HHS R01 DK132338NIDDK NIH HHS R01 DK135597NIMH NIH HHS RF1 MH125931
6 · The paper itself

Abstract

Moving from animal models to human applications in preclinical research encompasses a broad spectrum of disciplines in medical science. A fundamental element in the development of new drugs, treatments, diagnostic methods, and in deepening our understanding of disease processes is the accurate measurement of kidney tissues. Past studies have demonstrated the viability of translating glomeruli segmentation techniques from mouse models to human applications. Yet, these investigations tend to neglect the complexities involved in segmenting pathological glomeruli affected by different lesions. Such lesions present a wider range of morphological variations compared to healthy glomerular tissue, which are arguably more valuable than normal glomeruli in clinical practice. Furthermore, data on lesions from animal models can be more readily scaled up from disease models and whole kidney biopsies. This brings up a question: "

Indexed as

glomerular lesionglomerulussegmentationtransfer learingwhole-slide image

Identifiers

PMID41323934
PMCPMC12664535

What OpenQuestion holds

Textmetadata
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Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.