2015
DOI: 10.1016/j.media.2015.08.004
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Joint segmentation of lumen and outer wall from femoral artery MR images: Towards 3D imaging measurements of peripheral arterial disease

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Cited by 8 publications
(6 citation statements)
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“…Most currently available tube-tracing software is designed to analyze filled tubes. 41,42 Thus, to quantify our 3D time-lapse in vivo images, we developed custom tracing software for hollow tubes to extract vascular parameters including network topology, branch point position, and vessel segment length and diameter ( Figure 1 By combining these genetic, in vivo imaging, and image analysis techniques, we have sought to ask how the adult cerebral vasculature changes with time and in response to voluntary exercise. Pericyte distribution on capillary segments…”
Section: In Vivo Imaging Of Cerebral Vasculature In Tie2-cre:mtmg Micementioning
confidence: 99%
“…Most currently available tube-tracing software is designed to analyze filled tubes. 41,42 Thus, to quantify our 3D time-lapse in vivo images, we developed custom tracing software for hollow tubes to extract vascular parameters including network topology, branch point position, and vessel segment length and diameter ( Figure 1 By combining these genetic, in vivo imaging, and image analysis techniques, we have sought to ask how the adult cerebral vasculature changes with time and in response to voluntary exercise. Pericyte distribution on capillary segments…”
Section: In Vivo Imaging Of Cerebral Vasculature In Tie2-cre:mtmg Micementioning
confidence: 99%
“…In the same year, Ukwatta et al proposed another semi-automatic algorithm-coupled continuous max-flow (CCMF) model, to jointly segment the femoral artery’s lumen and outer wall surface. Their model demonstrated both high accuracy (Dice similarity coefficients ≥ 87% for both the lumen and outer wall surfaces) and high reproducibility (intra-class correlation coefficient of 0.95 for generating vessel wall area) [ 95 ]. Mistelbauer et al found that the application of semi-automatic lower limb vessel segmentation tools to clinical workflow enabled expert physicians to readily identify all clinically relevant lower extremity arteries with an average sensitivity of 92.9%, an average specificity, and an overall accuracy of 99.9% while saving 39% of the time [ 96 ].…”
Section: Magnetic Resonance Imaging (Mri)mentioning
confidence: 99%
“…Capillary vessels (< 50μm) appear as filled near tubular structures, whereas arteries and veins appear as non tubular empty structures. A few studies tackled the particular case of automatic segmentation of such structures (e.g., [33,34]), but we chose to manually segment these macrovessels to ensure a reliable vascular tree and geometries, which are essential for realistic flow simulations. Finally, the macrovascular network (illustrated in Fig 1) was merged with the microvascular network by adding a junction vessel by using the tensor voting method [30] as shown in Fig 1e, 1g and 1i.…”
Section: Network Extractionmentioning
confidence: 99%