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Validation and Development of a New Automatic Algorithm for Time-Resolved Segmentation of the Left Ventricle in Magnetic Resonance Imaging.

Tufvesson, Jane LU ; Hedström, Erik LU ; Steding Ehrenborg, Katarina LU ; Carlsson, Marcus LU ; Arheden, Håkan LU and Heiberg, Einar LU (2015) In BioMed Research International 2015.
Abstract
Introduction. Manual delineation of the left ventricle is clinical standard for quantification of cardiovascular magnetic resonance images despite being time consuming and observer dependent. Previous automatic methods generally do not account for one major contributor to stroke volume, the long-axis motion. Therefore, the aim of this study was to develop and validate an automatic algorithm for time-resolved segmentation covering the whole left ventricle, including basal slices affected by long-axis motion. Methods. Ninety subjects imaged with a cine balanced steady state free precession sequence were included in the study (training set n = 40, test set n = 50). Manual delineation was reference standard and second observer analysis was... (More)
Introduction. Manual delineation of the left ventricle is clinical standard for quantification of cardiovascular magnetic resonance images despite being time consuming and observer dependent. Previous automatic methods generally do not account for one major contributor to stroke volume, the long-axis motion. Therefore, the aim of this study was to develop and validate an automatic algorithm for time-resolved segmentation covering the whole left ventricle, including basal slices affected by long-axis motion. Methods. Ninety subjects imaged with a cine balanced steady state free precession sequence were included in the study (training set n = 40, test set n = 50). Manual delineation was reference standard and second observer analysis was performed in a subset (n = 25). The automatic algorithm uses deformable model with expectation-maximization, followed by automatic removal of papillary muscles and detection of the outflow tract. Results. The mean differences between automatic segmentation and manual delineation were EDV -11 mL, ESV 1 mL, EF -3%, and LVM 4 g in the test set. Conclusions. The automatic LV segmentation algorithm reached accuracy comparable to interobserver for manual delineation, thereby bringing automatic segmentation one step closer to clinical routine. The algorithm and all images with manual delineations are available for benchmarking. (Less)
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published
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BioMed Research International
volume
2015
publisher
Hindawi Publishing Corporation
external identifiers
  • pmid:26180818
  • wos:000357468600001
  • scopus:84936767063
ISSN
2314-6133
DOI
10.1155/2015/970357
language
English
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yes
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f3ac812e-4462-4cfe-985b-03c814e623a5 (old id 7749364)
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http://www.ncbi.nlm.nih.gov/pubmed/26180818?dopt=Abstract
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2015-08-09 01:05:36
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2017-10-01 03:08:21
@article{f3ac812e-4462-4cfe-985b-03c814e623a5,
  abstract     = {Introduction. Manual delineation of the left ventricle is clinical standard for quantification of cardiovascular magnetic resonance images despite being time consuming and observer dependent. Previous automatic methods generally do not account for one major contributor to stroke volume, the long-axis motion. Therefore, the aim of this study was to develop and validate an automatic algorithm for time-resolved segmentation covering the whole left ventricle, including basal slices affected by long-axis motion. Methods. Ninety subjects imaged with a cine balanced steady state free precession sequence were included in the study (training set n = 40, test set n = 50). Manual delineation was reference standard and second observer analysis was performed in a subset (n = 25). The automatic algorithm uses deformable model with expectation-maximization, followed by automatic removal of papillary muscles and detection of the outflow tract. Results. The mean differences between automatic segmentation and manual delineation were EDV -11 mL, ESV 1 mL, EF -3%, and LVM 4 g in the test set. Conclusions. The automatic LV segmentation algorithm reached accuracy comparable to interobserver for manual delineation, thereby bringing automatic segmentation one step closer to clinical routine. The algorithm and all images with manual delineations are available for benchmarking.},
  articleno    = {970357},
  author       = {Tufvesson, Jane and Hedström, Erik and Steding Ehrenborg, Katarina and Carlsson, Marcus and Arheden, Håkan and Heiberg, Einar},
  issn         = {2314-6133},
  language     = {eng},
  publisher    = {Hindawi Publishing Corporation},
  series       = {BioMed Research International},
  title        = {Validation and Development of a New Automatic Algorithm for Time-Resolved Segmentation of the Left Ventricle in Magnetic Resonance Imaging.},
  url          = {http://dx.doi.org/10.1155/2015/970357},
  volume       = {2015},
  year         = {2015},
}