基于多维信息融合的无监督快速肺部血管分割算法

doi:10.3969/j.issn.0258-8021.2023.03.005

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Abstract The segmentation of vessels in lung CT images plays an important role in the diagnosis and surgical treatment of diseases. In medical image related tasks, deep learning is widely used due to their strong feature representation and discriminative learning abilities. However, deep learning-based methods requires expensive GPUs and large amount of labeled data. In order to achieve better balance between the accuracy and efficiency of vessel segmentation in lung CT images, in this paper, a faster and more effective unsupervised vessel segmentation algorithm based onmulti-dimensional information fusion (MDF) was proposed. The algorithm designed 2D segmentation branches and 3D segmentation branches to make full use of 2D and 3D information, and combined the results of multiple branches in the final segmentation results, which can be quickly and effectively integrated into the traditional unsupervised algorithm. Meanwhile, MDF has strong parallelism capability and can be significantly accelerated on GPU. The proposed MDF based vessel segmentation algorithm was evaluated on the challenging VESSEL12 dataset and CARVE14 dataset comprehensively. The experimental results showed that MDF segmented the vessels in lung CT images with higher accuracy compared with other unsupervised methods. On the CARVE14 dataset, the DSC coefficient of vessels reached 0.716. Moreover, the MDF inference speed was roughly 20 times faster than Frangi algorithms, a multiscale algorithm based on Hession matrix, by GPU parallel optimization. Compared with deep learning-based methods, the segmentation performance of MDF exhibited stronger adaptation ability.

Key words： vessel segmentation deep learning multi-dimensional information fusion performance optimization

Received: 20 March 2022

PACS:

R318

Corresponding Authors: ^* E-mail: zmbhou@163.com

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	Articles by authors
	Chen Huagui
	Zhou Lei
	Zhao Lian
	Cong Zhiyang

Cite this article:

Chen Huagui,Zhou Lei,Zhao Lian, et al. Faster Lung Vessel Segmentation Using Multi-Dimensional Information Fusion[J]. Chinese Journal of Biomedical Engineering, 2023, 42(3): 289-300.

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http://cjbme.csbme.org/EN/10.3969/j.issn.0258-8021.2023.03.005 OR http://cjbme.csbme.org/EN/Y2023/V42/I3/289

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