FK-means: automatic atrial fibrosis segmentation using fractal-guided K-means clustering with Voronoi-clipping feature extraction of anatomical structures.

IF 3.6 3区 生物学 Q1 BIOLOGY Interface Focus Pub Date : 2023-12-15 eCollection Date: 2023-12-06 DOI:10.1098/rsfs.2023.0033
Marjan Firouznia, Markus Henningsson, Carl-Johan Carlhäll
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Abstract

Assessment of left atrial (LA) fibrosis from late gadolinium enhancement (LGE) magnetic resonance imaging (MRI) adds to the management of patients with atrial fibrillation. 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 to the score obtained by a deep learning method (3D UNet) for clipping (0.74). The automatic fibrosis segmentation method, which uses the Voronoi clipping method, achieved a Dice score of 0.76. This outperformed a 3D UNet 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.

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FK-means:利用分形引导的 K-means 聚类和解剖结构的 Voronoi-clipping 特征提取,自动进行心房纤维化分割。
通过晚期钆增强(LGE)磁共振成像(MRI)评估左心房(LA)纤维化有助于心房颤动患者的治疗。然而,准确评估 LA 壁的纤维化仍具有挑战性。利用剪切技术排除 LA 附近的解剖结构可减少 LA 纤维化的误诊。我们开发了一种新颖的 FK-means 方法,用于自动剪切和自动纤维化分割。该方法将基于特征的 Voronoi 图与基于分形的分层 3D K-means 方法相结合。在 LGE-MRI 数据上应用了所提出的自动 Voronoi 剪切方法,其 Dice 得分为 0.75,与深度学习方法(3D UNet)在剪切方面获得的得分(0.74)相似。使用 Voronoi 剪切法的自动纤维化分割方法的 Dice 得分为 0.76。这一成绩优于用于剪切和纤维化分类的 3D UNet 方法,后者的 Dice 得分为 0.69。此外,所提出的自动纤维化分割方法在使用人工剪切解剖结构的情况下,Dice 得分为 0.90。研究结果表明,FK-均值自动分析方法可实现可靠的 LA 纤维化分割,而剪切心房附近的解剖结构可增加对心房纤维化的评估。
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来源期刊
Interface Focus
Interface Focus BIOLOGY-
CiteScore
9.20
自引率
0.00%
发文量
44
审稿时长
6-12 weeks
期刊介绍: Each Interface Focus themed issue is devoted to a particular subject at the interface of the physical and life sciences. Formed of high-quality articles, they aim to facilitate cross-disciplinary research across this traditional divide by acting as a forum accessible to all. Topics may be newly emerging areas of research or dynamic aspects of more established fields. Organisers of each Interface Focus are strongly encouraged to contextualise the journal within their chosen subject.
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