Automatic nodule identification and differentiation in ultrasound videos to facilitate per-nodule examination
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arXiv
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| Autores principales: | , , , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2023
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866908669097017344 |
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| author | Jiang, Siyuan Ding, Yan Wang, Yuling Xu, Lei Dai, Wenli Chang, Wanru Zhang, Jianfeng Yu, Jie Zhou, Jianqiao Zhang, Chunquan Liang, Ping Kong, Dexing |
| author_facet | Jiang, Siyuan Ding, Yan Wang, Yuling Xu, Lei Dai, Wenli Chang, Wanru Zhang, Jianfeng Yu, Jie Zhou, Jianqiao Zhang, Chunquan Liang, Ping Kong, Dexing |
| contents | Ultrasound is a vital diagnostic technique in health screening, with the advantages of non-invasive, cost-effective, and radiation free, and therefore is widely applied in the diagnosis of nodules. However, it relies heavily on the expertise and clinical experience of the sonographer. In ultrasound images, a single nodule might present heterogeneous appearances in different cross-sectional views which makes it hard to perform per-nodule examination. Sonographers usually discriminate different nodules by examining the nodule features and the surrounding structures like gland and duct, which is cumbersome and time-consuming. To address this problem, we collected hundreds of breast ultrasound videos and built a nodule reidentification system that consists of two parts: an extractor based on the deep learning model that can extract feature vectors from the input video clips and a real-time clustering algorithm that automatically groups feature vectors by nodules. The system obtains satisfactory results and exhibits the capability to differentiate ultrasound videos. As far as we know, it's the first attempt to apply re-identification technique in the ultrasonic field. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2310_06339 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Automatic nodule identification and differentiation in ultrasound videos to facilitate per-nodule examination Jiang, Siyuan Ding, Yan Wang, Yuling Xu, Lei Dai, Wenli Chang, Wanru Zhang, Jianfeng Yu, Jie Zhou, Jianqiao Zhang, Chunquan Liang, Ping Kong, Dexing Image and Video Processing Machine Learning Ultrasound is a vital diagnostic technique in health screening, with the advantages of non-invasive, cost-effective, and radiation free, and therefore is widely applied in the diagnosis of nodules. However, it relies heavily on the expertise and clinical experience of the sonographer. In ultrasound images, a single nodule might present heterogeneous appearances in different cross-sectional views which makes it hard to perform per-nodule examination. Sonographers usually discriminate different nodules by examining the nodule features and the surrounding structures like gland and duct, which is cumbersome and time-consuming. To address this problem, we collected hundreds of breast ultrasound videos and built a nodule reidentification system that consists of two parts: an extractor based on the deep learning model that can extract feature vectors from the input video clips and a real-time clustering algorithm that automatically groups feature vectors by nodules. The system obtains satisfactory results and exhibits the capability to differentiate ultrasound videos. As far as we know, it's the first attempt to apply re-identification technique in the ultrasonic field. |
| title | Automatic nodule identification and differentiation in ultrasound videos to facilitate per-nodule examination |
| topic | Image and Video Processing Machine Learning |
| url | https://arxiv.org/abs/2310.06339 |