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Rapid automatic segmentation of abnormal tissue in late gadolinium enhancement cardiovascular magnetic resonance images for improved management of long-standing persistent atrial fibrillation

Overview of attention for article published in BioMedical Engineering OnLine, January 2015
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Title
Rapid automatic segmentation of abnormal tissue in late gadolinium enhancement cardiovascular magnetic resonance images for improved management of long-standing persistent atrial fibrillation
Published in
BioMedical Engineering OnLine, January 2015
DOI 10.1186/s12938-015-0083-8
Pubmed ID
Authors

Archontis Giannakidis, Eva Nyktari, Jennifer Keegan, Iain Pierce, Irina Suman Horduna, Shouvik Haldar, Dudley J. Pennell, Raad Mohiaddin, Tom Wong, David N. Firmin, Giannakidis, Archontis, Nyktari, Eva, Keegan, Jennifer, Pierce, Iain, Suman Horduna, Irina, Haldar, Shouvik, Pennell, Dudley J, Mohiaddin, Raad, Wong, Tom, Firmin, David N

Abstract

Atrial fibrillation (AF) is the most common heart rhythm disorder. In order for late Gd enhancement cardiovascular magnetic resonance (LGE CMR) to ameliorate the AF management, the ready availability of the accurate enhancement segmentation is required. However, the computer-aided segmentation of enhancement in LGE CMR of AF is still an open question. Additionally, the number of centres that have reported successful application of LGE CMR to guide clinical AF strategies remains low, while the debate on LGE CMR's diagnostic ability for AF still holds. The aim of this study is to propose a method that reliably distinguishes enhanced (abnormal) from non-enhanced (healthy) tissue within the left atrial wall of (pre-ablation and 3 months post-ablation) LGE CMR data-sets from long-standing persistent AF patients studied at our centre. Enhancement segmentation was achieved by employing thresholds benchmarked against the statistics of the whole left atrial blood-pool (LABP). The test-set cross-validation mechanism was applied to determine the input feature representation and algorithm that best predict enhancement threshold levels. Global normalized intensity threshold levels T PRE  = 1 1/4 and T POST  = 1 5/8 were found to segment enhancement in data-sets acquired pre-ablation and at 3 months post-ablation, respectively. The segmentation results were corroborated by using visual inspection of LGE CMR brightness levels and one endocardial bipolar voltage map. The measured extent of pre-ablation fibrosis fell within the normal range for the specific arrhythmia phenotype. 3D volume renderings of segmented post-ablation enhancement emulated the expected ablation lesion patterns. By comparing our technique with other related approaches that proposed different threshold levels (although they also relied on reference regions from within the LABP) for segmenting enhancement in LGE CMR data-sets of AF patients, we illustrated that the cut-off levels employed by other centres may not be usable for clinical studies performed in our centre. The proposed technique has great potential for successful employment in the AF management within our centre. It provides a highly desirable validation of the LGE CMR technique for AF studies. Inter-centre differences in the CMR acquisition protocol and image analysis strategy inevitably impede the selection of a universally optimal algorithm for segmentation of enhancement in AF studies.

Twitter Demographics

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Mendeley readers

The data shown below were compiled from readership statistics for 50 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 50 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 13 26%
Student > Master 8 16%
Student > Bachelor 5 10%
Researcher 4 8%
Student > Doctoral Student 3 6%
Other 3 6%
Unknown 14 28%
Readers by discipline Count As %
Medicine and Dentistry 14 28%
Engineering 7 14%
Computer Science 5 10%
Agricultural and Biological Sciences 3 6%
Physics and Astronomy 2 4%
Other 4 8%
Unknown 15 30%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 18 February 2016.
All research outputs
#5,172,638
of 7,211,484 outputs
Outputs from BioMedical Engineering OnLine
#230
of 398 outputs
Outputs of similar age
#147,436
of 237,996 outputs
Outputs of similar age from BioMedical Engineering OnLine
#13
of 16 outputs
Altmetric has tracked 7,211,484 research outputs across all sources so far. This one is in the 24th percentile – i.e., 24% of other outputs scored the same or lower than it.
So far Altmetric has tracked 398 research outputs from this source. They receive a mean Attention Score of 2.6. This one is in the 35th percentile – i.e., 35% of its peers scored the same or lower than it.
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We're also able to compare this research output to 16 others from the same source and published within six weeks on either side of this one. This one is in the 18th percentile – i.e., 18% of its contemporaries scored the same or lower than it.