Evidence map›Paper›PMID 37160979›Full record

ArticleScientific reports2023

A hybrid approach to full-scale reconstruction of renal arterial network.

Peidi Xu, Niels-Henrik Holstein-Rathlou, Stinne Byrholdt Søgaard, Carsten Gundlach, Charlotte Mehlin Sørensen, Kenny Erleben, Olga Sosnovtseva, Sune Darkner

Open access · goldAbstract read
In one paragraph

Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
3.1field-weighted citation impact, top 8% of its field
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

6 citing papers in PubMed, 12 citations in OpenAlex.

  1. Article
  2. Review
  3. Article
  4. Towards Fine-grained Renal Vasculature Segmentation: Full-Scale Hierarchical Learning with FH-Seg.Proceedings of SPIE--the International Society for Optical Engineering · 2025
    Article
  5. Article
  6. Article
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

8 authors at 2 institutions in 1 country.

Peidi XuDepartment of Computer Science, University of Copenhagen, Universitetsparken 1, Copenhagen, 2100, Denmark. peidi@di.ku.dk.
Niels-Henrik Holstein-RathlouDepartment of Biomedical Sciences, University of Copenhagen, Blegdamsvej 3B, Copenhagen, 2200, Denmark.
Stinne Byrholdt SøgaardDepartment of Biomedical Sciences, University of Copenhagen, Blegdamsvej 3B, Copenhagen, 2200, Denmark.
Carsten GundlachDepartment of Physics, Technical University of Denmark, Kongens Lyngby, Copenhagen, 2800, Denmark.
Charlotte Mehlin SørensenDepartment of Biomedical Sciences, University of Copenhagen, Blegdamsvej 3B, Copenhagen, 2200, Denmark.
Kenny ErlebenDepartment of Computer Science, University of Copenhagen, Universitetsparken 1, Copenhagen, 2100, Denmark.
Olga Sosnovtseva *Department of Biomedical Sciences, University of Copenhagen, Blegdamsvej 3B, Copenhagen, 2200, Denmark.
Sune Darkner *Department of Computer Science, University of Copenhagen, Universitetsparken 1, Copenhagen, 2100, Denmark.
University of Copenhagen · DKTechnical University of Denmark · DK

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The renal vasculature, acting as a resource distribution network, plays an important role in both the physiology and pathophysiology of the kidney. However, no imaging techniques allow an assessment of the structure and function of the renal vasculature due to limited spatial and temporal resolution. To develop realistic computer simulations of renal function, and to develop new image-based diagnostic methods based on artificial intelligence, it is necessary to have a realistic full-scale model of the renal vasculature. We propose a hybrid framework to build subject-specific models of the renal vascular network by using semi-automated segmentation of large arteries and estimation of cortex area from a micro-CT scan as a starting point, and by adopting the Global Constructive Optimization algorithm for generating smaller vessels. Our results show a close agreement between the reconstructed vasculature and existing anatomical data obtained from a rat kidney with respect to morphometric and hemodynamic parameters.

Indexed as

Acceptance and Commitment TherapyArtificial IntelligenceAnimalsArteriesKidneyRatsX-Ray Microtomography

Identifiers

PMID37160979
PMCPMC10169837
OpenAlexW4376114525

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.