ArticleJournal of medical systems2016
Accurate Measurement of Cross-Sectional Area of Femoral Artery on MRI Sequences of Transcontinental Ultramarathon Runners Using Optimal Parameters Selection.
Article in Journal of medical systems, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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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
2 citing papers in PubMed.
- Lesion-Based Bone Metastasis Detection in Chest Bone Scintigraphy Images of Prostate Cancer Patients Using Pre-Train, Negative Mining, and Deep Learning.Diagnostics (Basel, Switzerland) · 2021Article
- Organ Contouring for Lung Cancer Patients with a Seed Generation Scheme and Random Walks.Sensors (Basel, Switzerland) · 2020Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In clinics an accurate vessel segmentation method is important to quantize the vessel volume change with respect to time for artery elasticity measurement. This study proposes a modified version on 3D-expanded dynamic programming to find an optimal surface in a 3D matrix. The aim of this study is to discover the robustness against noises in measuring the cross-sectional area of the femoral artery on MRI datasets of ultra-endurance runners as accurately as possible. To do this, we use phantom images with different added noises and different image contrasts to find out the optimal parameters using grid search. The contrast between the vessel lumen and its background in phantom study is changed to simulate the real MRI dataset. We also add a plaque in phantom images to test the accuracy of the proposed algorithm in dealing pathologic cases. The phantom studies and grid search on selecting optimal parameters can offer an alternative way on parameter selection. In application to MRI, the accuracy is performed via comparisons between the manual tracings of experts and automated results. The mean relative error is 2.1 % ± 2.1 % on testing 11 MRI datasets (total 550 images). The phantom studies and grid search on selecting optimal parameters can offer an alternative way on parameter selection.
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Registered trials
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