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

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.

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

The dataset was originally published in DiVA and moved to SND in 2024. Show less..