Few-shot Periodic Video Image Segmentation Based on LSTM and Cross-attention Mechanism
With the development of modern video technology, periodic motion video image segmentation has important applications in motion analysis, medical imaging, and other fields. In this study, we designed a novel periodic motion detection and segmentation network based on deep learning technology, which c...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Editorial Office of Computerized Tomography Theory and Application
2025-07-01
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| Series: | CT Lilun yu yingyong yanjiu |
| Subjects: | |
| Online Access: | https://www.cttacn.org.cn/cn/article/doi/10.15953/j.ctta.2024.033 |
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| Summary: | With the development of modern video technology, periodic motion video image segmentation has important applications in motion analysis, medical imaging, and other fields. In this study, we designed a novel periodic motion detection and segmentation network based on deep learning technology, which combines the convolutional long short term memory network (ConvLSTM) and cross-attention mechanism. With relatively few labels, we can effectively capture the spatiotemporal context information of the objects of interest in the video sequence, achieving cross-frame consistency and accurate segmentation. Experimental results show that the proposed method performs well on periodic motion video datasets with few sample labels. In an ordinary video, the average region similarity and contour accuracy were 67.51% and 72.97%. respectively, which improved by 1%~1.5% than those obtained with the traditional method. In medical videos, the average region similarity and contour accuracy were 59.93% and 90.56%, respectively. Compared with DAN and Unet, the proposed method increased the regional similarity by 12.92% and 8.85%, whereas it improved the contour accuracy by 20.09% and 12.89%, respectively, thus achieving higher accuracy and stability. |
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| ISSN: | 1004-4140 |