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Automated left atrial time-resolved segmentation in MRI long-axis cine images using active contours
BACKGROUND: Segmentation of the left atrium (LA) is required to evaluate atrial size and function, which are important imaging biomarkers for a wide range of cardiovascular conditions, such as atrial fibrillation, stroke, and diastolic dysfunction. LA segmentations are currently being performed manu...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8214286/ https://www.ncbi.nlm.nih.gov/pubmed/34147081 http://dx.doi.org/10.1186/s12880-021-00630-3 |
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author | Gonzales, Ricardo A. Seemann, Felicia Lamy, Jérôme Arvidsson, Per M. Heiberg, Einar Murray, Victor Peters, Dana C. |
author_facet | Gonzales, Ricardo A. Seemann, Felicia Lamy, Jérôme Arvidsson, Per M. Heiberg, Einar Murray, Victor Peters, Dana C. |
author_sort | Gonzales, Ricardo A. |
collection | PubMed |
description | BACKGROUND: Segmentation of the left atrium (LA) is required to evaluate atrial size and function, which are important imaging biomarkers for a wide range of cardiovascular conditions, such as atrial fibrillation, stroke, and diastolic dysfunction. LA segmentations are currently being performed manually, which is time-consuming and observer-dependent. METHODS: This study presents an automated image processing algorithm for time-resolved LA segmentation in cardiac magnetic resonance imaging (MRI) long-axis cine images of the 2-chamber (2ch) and 4-chamber (4ch) views using active contours. The proposed algorithm combines mitral valve tracking, automated threshold calculation, edge detection on a radially resampled image, edge tracking based on Dijkstra’s algorithm, and post-processing involving smoothing and interpolation. The algorithm was evaluated in 37 patients diagnosed mainly with paroxysmal atrial fibrillation. Segmentation accuracy was assessed using the Dice similarity coefficient (DSC) and Hausdorff distance (HD), with manual segmentations in all time frames as the reference standard. For inter-observer variability analysis, a second observer performed manual segmentations at end-diastole and end-systole on all subjects. RESULTS: The proposed automated method achieved high performance in segmenting the LA in long-axis cine sequences, with a DSC of 0.96 for 2ch and 0.95 for 4ch, and an HD of 5.5 mm for 2ch and 6.4 mm for 4ch. The manual inter-observer variability analysis had an average DSC of 0.95 and an average HD of 4.9 mm. CONCLUSION: The proposed automated method achieved performance on par with human experts analyzing MRI images for evaluation of atrial size and function. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12880-021-00630-3. |
format | Online Article Text |
id | pubmed-8214286 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-82142862021-06-23 Automated left atrial time-resolved segmentation in MRI long-axis cine images using active contours Gonzales, Ricardo A. Seemann, Felicia Lamy, Jérôme Arvidsson, Per M. Heiberg, Einar Murray, Victor Peters, Dana C. BMC Med Imaging Research BACKGROUND: Segmentation of the left atrium (LA) is required to evaluate atrial size and function, which are important imaging biomarkers for a wide range of cardiovascular conditions, such as atrial fibrillation, stroke, and diastolic dysfunction. LA segmentations are currently being performed manually, which is time-consuming and observer-dependent. METHODS: This study presents an automated image processing algorithm for time-resolved LA segmentation in cardiac magnetic resonance imaging (MRI) long-axis cine images of the 2-chamber (2ch) and 4-chamber (4ch) views using active contours. The proposed algorithm combines mitral valve tracking, automated threshold calculation, edge detection on a radially resampled image, edge tracking based on Dijkstra’s algorithm, and post-processing involving smoothing and interpolation. The algorithm was evaluated in 37 patients diagnosed mainly with paroxysmal atrial fibrillation. Segmentation accuracy was assessed using the Dice similarity coefficient (DSC) and Hausdorff distance (HD), with manual segmentations in all time frames as the reference standard. For inter-observer variability analysis, a second observer performed manual segmentations at end-diastole and end-systole on all subjects. RESULTS: The proposed automated method achieved high performance in segmenting the LA in long-axis cine sequences, with a DSC of 0.96 for 2ch and 0.95 for 4ch, and an HD of 5.5 mm for 2ch and 6.4 mm for 4ch. The manual inter-observer variability analysis had an average DSC of 0.95 and an average HD of 4.9 mm. CONCLUSION: The proposed automated method achieved performance on par with human experts analyzing MRI images for evaluation of atrial size and function. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12880-021-00630-3. BioMed Central 2021-06-19 /pmc/articles/PMC8214286/ /pubmed/34147081 http://dx.doi.org/10.1186/s12880-021-00630-3 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Gonzales, Ricardo A. Seemann, Felicia Lamy, Jérôme Arvidsson, Per M. Heiberg, Einar Murray, Victor Peters, Dana C. Automated left atrial time-resolved segmentation in MRI long-axis cine images using active contours |
title | Automated left atrial time-resolved segmentation in MRI long-axis cine images using active contours |
title_full | Automated left atrial time-resolved segmentation in MRI long-axis cine images using active contours |
title_fullStr | Automated left atrial time-resolved segmentation in MRI long-axis cine images using active contours |
title_full_unstemmed | Automated left atrial time-resolved segmentation in MRI long-axis cine images using active contours |
title_short | Automated left atrial time-resolved segmentation in MRI long-axis cine images using active contours |
title_sort | automated left atrial time-resolved segmentation in mri long-axis cine images using active contours |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8214286/ https://www.ncbi.nlm.nih.gov/pubmed/34147081 http://dx.doi.org/10.1186/s12880-021-00630-3 |
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