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Long-term liver lesion tracking in contrast-enhanced ultrasound videos via a siamese network with temporal motion attention
Propose: Contrast-enhanced ultrasound has shown great promises for diagnosis and monitoring in a wide range of clinical conditions. Meanwhile, to obtain accurate and effective location of lesion in contrast-enhanced ultrasound videos is the basis for subsequent diagnosis and qualitative treatment, w...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10330811/ https://www.ncbi.nlm.nih.gov/pubmed/37435311 http://dx.doi.org/10.3389/fphys.2023.1180713 |
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author | Tian, Haozhe Cai, Wenjia Ding, Wenzhen Liang, Ping Yu, Jie Huang, Qinghua |
author_facet | Tian, Haozhe Cai, Wenjia Ding, Wenzhen Liang, Ping Yu, Jie Huang, Qinghua |
author_sort | Tian, Haozhe |
collection | PubMed |
description | Propose: Contrast-enhanced ultrasound has shown great promises for diagnosis and monitoring in a wide range of clinical conditions. Meanwhile, to obtain accurate and effective location of lesion in contrast-enhanced ultrasound videos is the basis for subsequent diagnosis and qualitative treatment, which is a challenging task nowadays. Methods: We propose to upgrade a siamese architecture-based neural network for robust and accurate landmark tracking in contrast-enhanced ultrasound videos. Due to few researches on it, the general inherent assumptions of the constant position model and the missing motion model remain unaddressed limitations. In our proposed model, we overcome these limitations by introducing two modules into the original architecture. We use a temporal motion attention based on Lucas Kanade optic flow and Karman filter to model the regular movement and better instruct location prediction. Moreover, we design a pipeline of template update to ensure timely adaptation to feature changes. Results: Eventually, the whole framework was performed on our collected datasets. It has achieved the average mean IoU values of 86.43% on 33 labeled videos with a total of 37,549 frames. In terms of tracking stability, our model has smaller TE of 19.2 pixels and RMSE of 27.6 with the FPS of 8.36 ± 3.23 compared to other classical tracking models. Conclusion: We designed and implemented a pipeline for tracking focal areas in contrast-enhanced ultrasound videos, which takes the siamese network as the backbone and uses optical flow and Kalman filter algorithm to provide position prior information. It turns out that these two additional modules are helpful for the analysis of CEUS videos. We hope that our work can provide an idea for the analysis of CEUS videos. |
format | Online Article Text |
id | pubmed-10330811 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-103308112023-07-11 Long-term liver lesion tracking in contrast-enhanced ultrasound videos via a siamese network with temporal motion attention Tian, Haozhe Cai, Wenjia Ding, Wenzhen Liang, Ping Yu, Jie Huang, Qinghua Front Physiol Physiology Propose: Contrast-enhanced ultrasound has shown great promises for diagnosis and monitoring in a wide range of clinical conditions. Meanwhile, to obtain accurate and effective location of lesion in contrast-enhanced ultrasound videos is the basis for subsequent diagnosis and qualitative treatment, which is a challenging task nowadays. Methods: We propose to upgrade a siamese architecture-based neural network for robust and accurate landmark tracking in contrast-enhanced ultrasound videos. Due to few researches on it, the general inherent assumptions of the constant position model and the missing motion model remain unaddressed limitations. In our proposed model, we overcome these limitations by introducing two modules into the original architecture. We use a temporal motion attention based on Lucas Kanade optic flow and Karman filter to model the regular movement and better instruct location prediction. Moreover, we design a pipeline of template update to ensure timely adaptation to feature changes. Results: Eventually, the whole framework was performed on our collected datasets. It has achieved the average mean IoU values of 86.43% on 33 labeled videos with a total of 37,549 frames. In terms of tracking stability, our model has smaller TE of 19.2 pixels and RMSE of 27.6 with the FPS of 8.36 ± 3.23 compared to other classical tracking models. Conclusion: We designed and implemented a pipeline for tracking focal areas in contrast-enhanced ultrasound videos, which takes the siamese network as the backbone and uses optical flow and Kalman filter algorithm to provide position prior information. It turns out that these two additional modules are helpful for the analysis of CEUS videos. We hope that our work can provide an idea for the analysis of CEUS videos. Frontiers Media S.A. 2023-06-26 /pmc/articles/PMC10330811/ /pubmed/37435311 http://dx.doi.org/10.3389/fphys.2023.1180713 Text en Copyright © 2023 Tian, Cai, Ding, Liang, Yu and Huang. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Physiology Tian, Haozhe Cai, Wenjia Ding, Wenzhen Liang, Ping Yu, Jie Huang, Qinghua Long-term liver lesion tracking in contrast-enhanced ultrasound videos via a siamese network with temporal motion attention |
title | Long-term liver lesion tracking in contrast-enhanced ultrasound videos via a siamese network with temporal motion attention |
title_full | Long-term liver lesion tracking in contrast-enhanced ultrasound videos via a siamese network with temporal motion attention |
title_fullStr | Long-term liver lesion tracking in contrast-enhanced ultrasound videos via a siamese network with temporal motion attention |
title_full_unstemmed | Long-term liver lesion tracking in contrast-enhanced ultrasound videos via a siamese network with temporal motion attention |
title_short | Long-term liver lesion tracking in contrast-enhanced ultrasound videos via a siamese network with temporal motion attention |
title_sort | long-term liver lesion tracking in contrast-enhanced ultrasound videos via a siamese network with temporal motion attention |
topic | Physiology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10330811/ https://www.ncbi.nlm.nih.gov/pubmed/37435311 http://dx.doi.org/10.3389/fphys.2023.1180713 |
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