Title |
Evaluation of current algorithms for segmentation of scar tissue from late Gadolinium enhancement cardiovascular magnetic resonance of the left atrium: an open-access grand challenge
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Published in |
Critical Reviews in Diagnostic Imaging, December 2013
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DOI | 10.1186/1532-429x-15-105 |
Pubmed ID | |
Authors |
Rashed Karim, R James Housden, Mayuragoban Balasubramaniam, Zhong Chen, Daniel Perry, Ayesha Uddin, Yosra Al-Beyatti, Ebrahim Palkhi, Prince Acheampong, Samantha Obom, Anja Hennemuth, YingLi Lu, Wenjia Bai, Wenzhe Shi, Yi Gao, Heinz-Otto Peitgen, Perry Radau, Reza Razavi, Allen Tannenbaum, Daniel Rueckert, Josh Cates, Tobias Schaeffter, Dana Peters, Rob MacLeod, Kawal Rhode |
Abstract |
Late Gadolinium enhancement (LGE) cardiovascular magnetic resonance (CMR) imaging can be used to visualise regions of fibrosis and scarring in the left atrium (LA) myocardium. This can be important for treatment stratification of patients with atrial fibrillation (AF) and for assessment of treatment after radio frequency catheter ablation (RFCA). In this paper we present a standardised evaluation benchmarking framework for algorithms segmenting fibrosis and scar from LGE CMR images. The algorithms reported are the response to an open challenge that was put to the medical imaging community through an ISBI (IEEE International Symposium on Biomedical Imaging) workshop. |
X Demographics
Geographical breakdown
Country | Count | As % |
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New Zealand | 1 | 25% |
United States | 1 | 25% |
Unknown | 2 | 50% |
Demographic breakdown
Type | Count | As % |
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Members of the public | 3 | 75% |
Scientists | 1 | 25% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
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United Kingdom | 4 | 2% |
Spain | 2 | 1% |
Cuba | 1 | <1% |
France | 1 | <1% |
New Zealand | 1 | <1% |
United States | 1 | <1% |
Unknown | 190 | 95% |
Demographic breakdown
Readers by professional status | Count | As % |
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Student > Ph. D. Student | 42 | 21% |
Researcher | 37 | 19% |
Student > Master | 21 | 11% |
Student > Doctoral Student | 13 | 7% |
Other | 12 | 6% |
Other | 44 | 22% |
Unknown | 31 | 16% |
Readers by discipline | Count | As % |
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Medicine and Dentistry | 57 | 28% |
Engineering | 40 | 20% |
Computer Science | 26 | 13% |
Physics and Astronomy | 6 | 3% |
Agricultural and Biological Sciences | 5 | 3% |
Other | 23 | 12% |
Unknown | 43 | 22% |