PURPOSE: To establish the accuracy, intra- and inter-observer variabilities of four different segmentation methods for measuring cardiac functional parameters in healthy and infarcted rat hearts. MATERIALS AND METHODS: Six Wistar rats were imaged before and after myocardial infarction using an electrocardiogram and respiratory-gated spoiled gradient echo sequence. Blinded and randomized datasets were analyzed by various semi-automatic and manual segmentation methods to compare their measurement bias and variability. In addition, the accuracy of these methods was assessed by comparison with reference measurements acquired from high-resolution three-dimensional (3D) datasets of a heart phantom. RESULTS: Relative inter- and intra-observer variability were found to be similar for all four methods. Semi-automatic segmentation methods reduced analysis time by up to 70%, while yielding similar measurement bias and variability compared with manual segmentation. Semi-automatic methods were found to underestimate the ejection fraction for healthy hearts compared with manual segmentation while overestimating them in infarcted hearts. However, semi-automatic segmentation of short axis slices agreed better with 3D reference scans of a heart phantom compared with manual segmentation. CONCLUSION: Semi-automatic segmentation methods are faster than manual segmentation, while offering a similar intra- and inter-observer variability. However, a potential bias has been observed between healthy and infarcted hearts for different methods, which should also be considered when selecting the most appropriate analysis technique.
PURPOSE: To establish the accuracy, intra- and inter-observer variabilities of four different segmentation methods for measuring cardiac functional parameters in healthy and infarctedrat hearts. MATERIALS AND METHODS: Six Wistar rats were imaged before and after myocardial infarction using an electrocardiogram and respiratory-gated spoiled gradient echo sequence. Blinded and randomized datasets were analyzed by various semi-automatic and manual segmentation methods to compare their measurement bias and variability. In addition, the accuracy of these methods was assessed by comparison with reference measurements acquired from high-resolution three-dimensional (3D) datasets of a heart phantom. RESULTS: Relative inter- and intra-observer variability were found to be similar for all four methods. Semi-automatic segmentation methods reduced analysis time by up to 70%, while yielding similar measurement bias and variability compared with manual segmentation. Semi-automatic methods were found to underestimate the ejection fraction for healthy hearts compared with manual segmentation while overestimating them in infarcted hearts. However, semi-automatic segmentation of short axis slices agreed better with 3D reference scans of a heart phantom compared with manual segmentation. CONCLUSION:Semi-automatic segmentation methods are faster than manual segmentation, while offering a similar intra- and inter-observer variability. However, a potential bias has been observed between healthy and infarcted hearts for different methods, which should also be considered when selecting the most appropriate analysis technique.
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Authors: Steven Fortune; Maurits A Jansen; Tom Anderson; Gillian A Gray; Jürgen E Schneider; Peter R Hoskins; Ian Marshall Journal: Magn Reson Imaging Date: 2012-07-06 Impact factor: 2.546
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Authors: Peter Nordbeck; Leoni Bönhof; Karl-Heinz Hiller; Sabine Voll; Paula Arias-Loza; Lea Seidlmayer; Tatjana Williams; Yu-Xiang Ye; Daniel Gensler; Theo Pelzer; Georg Ertl; Peter M Jakob; Wolfgang R Bauer; Oliver Ritter Journal: PLoS One Date: 2013-08-21 Impact factor: 3.240
Authors: Muhammad G Saleh; Sarah-Kate Sharp; Alkathafi Alhamud; Bruce S Spottiswoode; Andre J W van der Kouwe; Neil H Davies; Thomas Franz; Ernesta M Meintjes Journal: J Biomed Biotechnol Date: 2012-10-04
Authors: Anna P Hnatiuk; Sang-Ging Ong; Fernanda D Olea; Paola Locatelli; Johannes Riegler; Won Hee Lee; Cheng Hao Jen; Andrea De Lorenzi; Carlos S Giménez; Rubén Laguens; Joseph C Wu; Alberto Crottogini Journal: J Am Heart Assoc Date: 2016-07-06 Impact factor: 5.501