Automatic segmentation of intraluminal thrombosis of abdominal aortic aneurysms from CT angiography using a mixed-scale-driven multiview perception network (M

Zonghan Lyu, Nan Mu, Mostafa Rezaeitaleshmahalleh, Xiaoming Zhang, Robert McBane, Jingfeng Jiang

Abstract read

In one paragraph

Article in Computers in biology and medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it

0cells of the map it votes in

4citing 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

4 citing papers in PubMed.

Validation of a Deep Learning U-Net Algorithm for Multistructure Segmentation of Infrarenal Abdominal Aortic Aneurysms including Lumen, Thrombus, and Calcifications.EJVES vascular forum · 2026
Article
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 · 2025
Review
Integrated multidisciplinary approach to aneurysm hemodynamic analysis: numerical simulation,Frontiers in bioengineering and biotechnology · 2025
Review
Emerging Elastic Micro-Nano Materials for Diagnosis and Treatment of Thrombosis.Research (Washington, D.C.) · 2025
Review

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

6 authors.

Zonghan LyuBiomedical Engineering, Michigan Technological University, Houghton, MI, 49931, USA; Center for Biocomputing and Digital Health, Health Research Institute and Institute of Computing and Cybernetics, Michigan Technological University, Houghton, MI, 49931, USA.

Nan MuBiomedical Engineering, Michigan Technological University, Houghton, MI, 49931, USA; Center for Biocomputing and Digital Health, Health Research Institute and Institute of Computing and Cybernetics, Michigan Technological University, Houghton, MI, 49931, USA.

Mostafa RezaeitaleshmahallehBiomedical Engineering, Michigan Technological University, Houghton, MI, 49931, USA; Center for Biocomputing and Digital Health, Health Research Institute and Institute of Computing and Cybernetics, Michigan Technological University, Houghton, MI, 49931, USA.

Xiaoming ZhangMayo Clinic, Rochester, MN, 55902, USA.

Robert McBaneMayo Clinic, Rochester, MN, 55902, USA.

Jingfeng JiangBiomedical Engineering, Michigan Technological University, Houghton, MI, 49931, USA; Center for Biocomputing and Digital Health, Health Research Institute and Institute of Computing and Cybernetics, Michigan Technological University, Houghton, MI, 49931, USA; Mayo Clinic, Rochester, MN, 55902, USA. Electronic address: jjiang1@mtu.edu.

Funding

Personalized Management of Intracranial Aneurysms Using Computer-aided AnalyticsR01EB029570 · NIBIB · MICHIGAN TECHNOLOGICAL UNIVERSITY · PI JIANG, JINGFENG · 2021 to 2024

$1.3M

American Heart Association-American Stroke Association 23POST1022454NIBIB NIH HHS R01 EB029570

6 · The paper itself

Abstract

Intraluminal thrombosis (ILT) plays a critical role in the progression of abdominal aortic aneurysms (AAA). Understanding the role of ILT can improve the evaluation and management of AAAs. However, compared with highly developed automatic vessel lumen segmentation methods, ILT segmentation is challenging. Angiographic contrast agents can enhance the vessel lumen but cannot improve boundary delineation of the ILT regions; the lack of intrinsic contrast in the ILT structure significantly limits the accurate segmentation of ILT. Additionally, ILT is not evenly distributed within AAAs; its sparsity and scattered distributions in the imaging data pose challenges to the learning process of neural networks. Thus, we propose a multiview fusion approach, allowing us to obtain high-quality ILT delineation from computed tomography angiography (CTA) data. Our multiview fusion network is named Mixed-scale-driven Multiview Perception Network (M

Indexed as

Aortic Aneurysm, AbdominalComputed Tomography AngiographyThrombosisHumansMaleNeural Networks, ComputerAbdominal aortic aneurysmDeep-learning neural networkImage segmentationIntraluminal thrombosisMultiview perception

Identifiers

PMID39033681

DOI10.1016/j.compbiomed.2024.108838

PMCPMC11772087

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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.