ArticleData in brief2022
Article in Data in brief, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.
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.
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.
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Who cites it
31 citing papers in PubMed, 64 citations in OpenAlex.
- HUG-VAS: A Hierarchical NURBS-Based Generative Model for Aortic Geometry Synthesis and Controllable Editing.Computer methods in applied mechanics and engineering · 2026Article
- Towards Fixing Vessel Segmentation Breakage: A Systematic Review.Journal of clinical medicine · 2026Review
- [A frequency-adaptive implicit neural representation method for medical image compression].Nan fang yi ke da xue xue bao = Journal of Southern Medical University · 2026Article
- A Dataset of Abdominal CT with Artery and Vein Segmentations for Colorectal Cancer Surgical Planning.Scientific data · 2026Article
- AortaExplorer: AI-driven analysis of the aorta in CT images.Medical & biological engineering & computing · 2026Article
- Hemodynamic Alterations Associated with Varying Aneurysm Sizes in the Aortic Arch.Bioengineering (Basel, Switzerland) · 2026Article
- AortaDiff: Volume-Guided Conditional Diffusion Models for Multi-Branch Aortic Surface Generation.IEEE transactions on visualization and computer graphics · 2026Article
- A hybrid CNN-Transformer network integrating multiscale spatially detailed features for medical image segmentation.PloS one · 2026Article
- AortaSeg-60: an open real-world CT-angiography dataset of the aorta with automated segmentation masks and pathological variability.GigaByte (Hong Kong, China) · 2026Article
- Automatic Segmentation of Intraluminal Thrombus in Abdominal Aortic Aneurysms Based on CT Images: A Comprehensive Review of Deep Learning-Based Methods.Journal of clinical medicine · 2025Review
- Automated AI detection of thoracic aortic dissection on CT imaging.European radiology experimental · 2025Article
- The ASC Module: A GPU Memory-Efficient, Physiology-Aware Approach for Improving Segmentation Accuracy on Poorly Contrast-Enhanced CT Scans-A Preliminary Study.Bioengineering (Basel, Switzerland) · 2025Article
- AI-powered automated model construction for patient-specific CFD simulations of aortic flows.Science advances · 2025Article
- Large-vocabulary segmentation for medical images with text prompts.NPJ digital medicine · 2025Article
- A Fully Automatic Pipeline of Identification, Segmentation, and Subtyping of Aortic Dissection from CT Angiography.Cardiovascular engineering and technology · 2025Article
- Unsupervised skull segmentation in MR images utilizing modality translation and super-resolution.Scientific reports · 2025Article
- Automatic CTA analysis for blood vessels and aneurysm features extraction in EVAR planning.Scientific reports · 2025Article
- HIERARCHICAL LOG BAYESIAN NEURAL NETWORK FOR ENHANCED AORTA SEGMENTATION.Proceedings. IEEE International Symposium on Biomedical Imaging · 2025Article
- Article
- Rapid and automatic hemodynamic assessment: integration of deep learning-based image segmentation, vessel reconstruction, and CFD prediction.Quantitative imaging in medicine and surgery · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 3 institutions in 2 countries.
Funding
Abstract
In this article, we present a multicenter aortic vessel tree database collection, containing 56 aortas and their branches. The datasets have been acquired with computed tomography angiography (CTA) scans and each scan covers the ascending aorta, the aortic arch and its branches into the head/neck area, the thoracic aorta, the abdominal aorta and the lower abdominal aorta with the iliac arteries branching into the legs. For each scan, the collection provides a semi-automatically generated segmentation mask of the aortic vessel tree (ground truth). The scans come from three different collections and various hospitals, having various resolutions, which enables studying the geometry/shape variabilities of human aortas and its branches from different geographic locations. Furthermore, creating a robust statistical model of the shape of human aortic vessel trees, which can be used for various tasks such as the development of fully-automatic segmentation algorithms for new, unseen aortic vessel tree cases, e.g. by training deep learning-based approaches. Hence, the collection can serve as an evaluation set for automatic aortic vessel tree segmentation algorithms.
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Registered trials
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.