Evidence map›Paper›PMID 39070623›Full record

ArticleResearch square2024

Deep Learning for 3D Vascular Segmentation in Phase Contrast Tomography.

Ekin Yagis, Shahab Aslani, Yashvardhan Jain, Yang Zhou, Shahrokh Rahmani, Joseph Brunet, Alexandre Bellier, Christopher Werlein, Maximilian Ackermann, Danny Jonigk and 3 more

Abstract readPreprint
PubMed Full text at PMC Publisher
In one paragraph

Article in Research square, 2024. 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

5 · Who and what money

Authors and funding

13 authors.

Ekin YagisDepartment of Mechanical Engineering, University College London, London, UK.
Shahab AslaniDepartment of Mechanical Engineering, University College London, London, UK.
Yashvardhan JainDepartment of Intelligent Systems Engineering, Luddy School of Informatics, Computing, and Engineering, Indiana University, Bloomington, USA.
Yang ZhouDepartment of Mechanical Engineering, University College London, London, UK.
Shahrokh RahmaniDepartment of Mechanical Engineering, University College London, London, UK.
Joseph BrunetDepartment of Mechanical Engineering, University College London, London, UK.
Alexandre BellierLaboratoire d'Anatomie Des Alpes Françaises, Grenoble, France.
Christopher WerleinInstitute of Pathology, Hannover Medical School, Carl-Neuberg-Straße 1, 30625, Hannover, Germany.
Maximilian AckermannDepartment of Mechanical Engineering, University College London, London, UK.
Danny JonigkMember of the German Center for Lung Research (DZL), Biomedical Research in Endstage and Obstructive Lung Disease Hannover (BREATH), Hannover, Germany.
Paul TafforeauEuropean Synchrotron Radiation Facility, Grenoble, France.
Peter D LeeDepartment of Mechanical Engineering, University College London, London, UK.
Claire WalshDepartment of Mechanical Engineering, University College London, London, UK.

Funding

SenNet Supplement - Consortium BenchmarkingU24CA268108 · NCI · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI Philip D. Blood, JONATHAN C. SILVERSTEIN · 2021 to 2026
$22.1M
ConProject-002U2CDK114886 · NIDDK · UNIVERSITY OF WASHINGTON · PI KRETZLER, MATTHIAS · 2017 to 2021
$21.0M
KPMP Kidney Mapping and Atlas Project (KMAP)U01DK133090 · NIDDK · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI Jonathan Himmelfarb, Matthias Kretzler · 2022 to 2026
$10.4M
3D Multiscale Biomolecular Human Reference Atlas Construction, Visualization and Usage [4 of 5]OT2OD033756 · OD · TRUSTEES OF INDIANA UNIVERSITY · PI BORNER, KATY · 2022 to 2025
$7.4M
The Human Body Atlas: High-Resolution, Functional Mapping of Voxel, Vector, and Meta DatasetsOT2OD026671 · OD · TRUSTEES OF INDIANA UNIVERSITY · PI BORNER, KATY · 2018 to 2021
$3.5M
National Institute of Diabetes and Digestive and Kidney Diseases ATLAS (D2K-ATLAS) Center as an accessible, comprehensive data portfolio for renal and genitourinary development and diseaseU24DK135157 · NIDDK · BRIGHAM AND WOMEN'S HOSPITAL · PI JAIN, SANJAY, VALERIUS, MICHAEL TODD · 2022 to 2023
$3.4M
NCI NIH HHS U24 CA268108NIDDK NIH HHS U01 DK133090NIDDK NIH HHS U24 DK135157NIDDK NIH HHS U2C DK114886NIH HHS OT2 OD026671NIH HHS OT2 OD033756Wellcome Trust
6 · The paper itself

Abstract

Automated blood vessel segmentation is critical for biomedical image analysis, as vessel morphology changes are associated with numerous pathologies. Still, precise segmentation is difficult due to the complexity of vascular structures, anatomical variations across patients, the scarcity of annotated public datasets, and the quality of images. Our goal is to provide a foundation on the topic and identify a robust baseline model for application to vascular segmentation using a new imaging modality, Hierarchical Phase-Contrast Tomography (HiP-CT). We begin with an extensive review of current machine learning approaches for vascular segmentation across various organs. Our work introduces a meticulously curated training dataset, verified by double annotators, consisting of vascular data from three kidneys imaged using Hierarchical Phase-Contrast Tomography (HiP-CT) as part of the Human Organ Atlas Project. HiP-CT, pioneered at the European Synchrotron Radiation Facility in 2020, revolutionizes 3D organ imaging by offering resolution around 20

Identifiers

PMID39070623
PMCPMC11276017

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Registered trials

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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.