基于无监督深度学习光流法的超声图像运动肌肉分割

doi:10.3969/j.issn.0258-8021.2025.04.006

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Abstract Accurate image segmentation is the foundation of image guided treatment for spastic muscles after stroke. We proposed an unsupervised deep learning optical flow algorithm, aiming to segment the moving target muscle. Due to lack of labels of ultrasound image dataset of motor muscles not like the previous static ultrasound image datasets, an unsupervised optical flow estimation framework was constructed. The optical flow was learned from the unlabeled image sequence through augemented self-monitoring. At the same time, in order to avoid the influence of view synthesis targets on accuracy of augmented data, another forward propagation was added to the converted image to distort the basic learning framework, in which the conversion prediction from the original image is monitored, and finally, Gaussian filter, speed threshold filtering, border detection filtering and convex hull segmentation were employed to extract moving muscles from the optical flow field. The proposed method was evaluated on MPI Sintel, KITTI2012, KITTI2015, Flying Chairs and CityScapes (totally including 6 000 basic samples and 3 600 multi frame model samples) for its basic performance and cross dataset generalization performance, and muscle ultrsound image dataset containing 600 samples for its transfer performance was tested. The results demonstrated that the proposed method effectively extracted the dense optical flow field information without the use of optical flow labels in the dataset. The average endpoint error reached 5.80, the parameter quantity was reduced to 2.35 M, and good cross dataset generalization ability was obsersed. In the transfer test, compared with the manual segmentation results, the mean of center point differences was less than 1 mm, and the mean of intersection over unions was higher than 0.9. The proposedmethod can effectively segment the moving musclesof ultrasound images.

Key words： target muscle segmentation dense optical flow convolutional neural network unsupervised learning

Received: 18 March 2022

PACS:

R318

Corresponding Authors: ^*E-mail:luankuan@hrbeu.edu.cn

About author:: ^#中国生物医学工程学会会员(Member, Chinese Society of Biomedical Engineering)

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	Li Yang
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Cite this article:

Li Yang,Li Jin,Luan Kuan. Muscle Ultrasound Image Segmentation Based on Unsupervised Deep Learning Optical Flow Method[J]. Chinese Journal of Biomedical Engineering, 2025, 44(4): 435-446.

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http://cjbme.csbme.org/EN/10.3969/j.issn.0258-8021.2025.04.006 OR http://cjbme.csbme.org/EN/Y2025/V44/I4/435

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