A Spatial-Temporal Progressive Fusion Network for Breast Lesion Segmentation in Ultrasound Videos
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866909139793346560 |
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| author | Tu, Zhengzheng Zhu, Zigang Duan, Yayang Jiang, Bo Wang, Qishun Zhang, Chaoxue |
| author_facet | Tu, Zhengzheng Zhu, Zigang Duan, Yayang Jiang, Bo Wang, Qishun Zhang, Chaoxue |
| contents | Ultrasound video-based breast lesion segmentation provides a valuable assistance in early breast lesion detection and treatment. However, existing works mainly focus on lesion segmentation based on ultrasound breast images which usually can not be adapted well to obtain desirable results on ultrasound videos. The main challenge for ultrasound video-based breast lesion segmentation is how to exploit the lesion cues of both intra-frame and inter-frame simultaneously. To address this problem, we propose a novel Spatial-Temporal Progressive Fusion Network (STPFNet) for video based breast lesion segmentation problem. The main aspects of the proposed STPFNet are threefold. First, we propose to adopt a unified network architecture to capture both spatial dependences within each ultrasound frame and temporal correlations between different frames together for ultrasound data representation. Second, we propose a new fusion module, termed Multi-Scale Feature Fusion (MSFF), to fuse spatial and temporal cues together for lesion detection. MSFF can help to determine the boundary contour of lesion region to overcome the issue of lesion boundary blurring. Third, we propose to exploit the segmentation result of previous frame as the prior knowledge to suppress the noisy background and learn more robust representation. In particular, we introduce a new publicly available ultrasound video breast lesion segmentation dataset, termed UVBLS200, which is specifically dedicated to breast lesion segmentation. It contains 200 videos, including 80 videos of benign lesions and 120 videos of malignant lesions. Experiments on the proposed dataset demonstrate that the proposed STPFNet achieves better breast lesion detection performance than state-of-the-art methods. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_11699 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Spatial-Temporal Progressive Fusion Network for Breast Lesion Segmentation in Ultrasound Videos Tu, Zhengzheng Zhu, Zigang Duan, Yayang Jiang, Bo Wang, Qishun Zhang, Chaoxue Image and Video Processing Computer Vision and Pattern Recognition Ultrasound video-based breast lesion segmentation provides a valuable assistance in early breast lesion detection and treatment. However, existing works mainly focus on lesion segmentation based on ultrasound breast images which usually can not be adapted well to obtain desirable results on ultrasound videos. The main challenge for ultrasound video-based breast lesion segmentation is how to exploit the lesion cues of both intra-frame and inter-frame simultaneously. To address this problem, we propose a novel Spatial-Temporal Progressive Fusion Network (STPFNet) for video based breast lesion segmentation problem. The main aspects of the proposed STPFNet are threefold. First, we propose to adopt a unified network architecture to capture both spatial dependences within each ultrasound frame and temporal correlations between different frames together for ultrasound data representation. Second, we propose a new fusion module, termed Multi-Scale Feature Fusion (MSFF), to fuse spatial and temporal cues together for lesion detection. MSFF can help to determine the boundary contour of lesion region to overcome the issue of lesion boundary blurring. Third, we propose to exploit the segmentation result of previous frame as the prior knowledge to suppress the noisy background and learn more robust representation. In particular, we introduce a new publicly available ultrasound video breast lesion segmentation dataset, termed UVBLS200, which is specifically dedicated to breast lesion segmentation. It contains 200 videos, including 80 videos of benign lesions and 120 videos of malignant lesions. Experiments on the proposed dataset demonstrate that the proposed STPFNet achieves better breast lesion detection performance than state-of-the-art methods. |
| title | A Spatial-Temporal Progressive Fusion Network for Breast Lesion Segmentation in Ultrasound Videos |
| topic | Image and Video Processing Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2403.11699 |