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Dataset and code for "FK-means: Automatic Atrial Fibrosis Segmentation using Fractal-guided K-means Clustering with Voronoi-Clipping Feature Extraction of Anatomical Structures": FKmeans for fibrosis segmentation
Linköping University, Department of Health, Medicine and Caring Sciences, Division of Diagnostics and Specialist Medicine. Linköping University, Faculty of Medicine and Health Sciences.ORCID iD: 0000-0001-6491-2955
Linköping University, Department of Health, Medicine and Caring Sciences, Division of Diagnostics and Specialist Medicine. Linköping University, Faculty of Medicine and Health Sciences.ORCID iD: 0000-0001-6142-3005
Linköping University, Department of Health, Medicine and Caring Sciences, Division of Diagnostics and Specialist Medicine. Linköping University, Faculty of Medicine and Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV). Region Östergötland, Heart Center, Department of Clinical Physiology in Linköping.ORCID iD: 0000-0003-2198-9690
2023 (English)Data setAlternative title
FKmeans (English)
Abstract [en]

Assessment of left atrial (LA) fibrosis from late gadolinium enhancement (LGE) magnetic resonance imaging (MRI) adds to the management of patients with atrial fibrillation (AF). However, accurate assessment of fibrosis in the LA wall remains challenging. Excluding anatomical structures in the LA proximity using clipping techniques can reduce misclassification of LA fibrosis. A novel FK-means approach for combined automatic clipping and automatic fibrosis segmentation was developed. This approach combines a feature-based Voronoi diagram with a hierarchical 3D K-means fractal-based method. The proposed automatic Voronoi clipping method was applied on LGE MRI data and achieved a Dice score of 0.75, similar as the score obtained by a deep learning method (3D UNet) for clipping (0.74). The automatic fibrosis segmentation method, which utilizes the Voronoi clipping method, achieved a Dice score of 0.76. This outperformed a 3D U-Net method for clipping and fibrosis classification, which had a Dice score of 0.69. Moreover, the proposed automatic fibrosis segmentation method achieved a Dice score of 0.90, using manual clipping of anatomical structures. The findings suggest that the automatic FK-means analysis approach enables reliable LA fibrosis segmentation and that clipping of anatomical structures in the atrial proximity can add to the assessment of atrial fibrosis. 

Place, publisher, year
Linköping University Electronic Press, 2023.
Version
Version 1.0
Keywords [en]
Fibrosis segmentation, Left atrium, Pulmonary veins, Mitral valve, Clipping, K-means, Deep learning
National Category
Cardiac and Cardiovascular Systems Medical Image Processing
Identifiers
URN: urn:nbn:se:liu:diva-199036DOI: 10.48360/m803-yp37OAI: oai:DiVA.org:liu-199036DiVA, id: diva2:1810436
Note

For access to data and code please contact datamanagement@liu.se for further information.

Available from: 2023-11-08 Created: 2023-11-08 Last updated: 2024-03-11

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Henningsson, MarkusCarlhäll, Carl-Johan

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Firouznia, MarjanHenningsson, MarkusCarlhäll, Carl-Johan
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Division of Diagnostics and Specialist MedicineFaculty of Medicine and Health SciencesCenter for Medical Image Science and Visualization (CMIV)Department of Clinical Physiology in Linköping
Cardiac and Cardiovascular SystemsMedical Image Processing
Firouznia, M., Henningsson, M. & Carlhäll, C. (2023). FK-means: automatic atrial fibrosis segmentation using fractal-guided K-means clustering with Voronoi-clipping feature extraction of anatomical structures. Interface Focus, 13(6), Article ID 20230033.

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