Self-supervised context-aware correlation filter for robust landmark tracking in liver ultrasound sequences
© 2024 Walter de Gruyter GmbH, Berlin/Boston..
OBJECTIVES: Respiratory motion-induced displacement of internal organs poses a significant challenge in image-guided radiation therapy, particularly affecting liver landmark tracking accuracy.
METHODS: Addressing this concern, we propose a self-supervised method for robust landmark tracking in long liver ultrasound sequences. Our approach leverages a Siamese-based context-aware correlation filter network, trained by using the consistency loss between forward tracking and back verification. By effectively utilizing both labeled and unlabeled liver ultrasound images, our model, Siam-CCF , mitigates the impact of speckle noise and artifacts on ultrasonic image tracking by a context-aware correlation filter. Additionally, a fusion strategy for template patch feature helps the tracker to obtain rich appearance information around the point-landmark.
RESULTS: Siam-CCF achieves a mean tracking error of 0.79 ± 0.83 mm at a frame rate of 118.6 fps, exhibiting a superior speed-accuracy trade-off on the public MICCAI 2015 Challenge on Liver Ultrasound Tracking (CLUST2015) 2D dataset. This performance won the 5th place on the CLUST2015 2D point-landmark tracking task.
CONCLUSIONS: Extensive experiments validate the effectiveness of our proposed approach, establishing it as one of the top-performing techniques on the CLUST2015 online leaderboard at the time of this submission.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - year:2024 |
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| Enthalten in: |
Biomedizinische Technik. Biomedical engineering - (2024) vom: 07. Feb. |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Ma, Lin [VerfasserIn] |
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| Links: |
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| Themen: |
Image-guided radiation therapy |
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| Anmerkungen: |
Date Revised 14.02.2024 published: Print-Electronic Citation Status Publisher |
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| doi: |
10.1515/bmt-2022-0489 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM36843205X |
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| 100 | 1 | |a Ma, Lin |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Self-supervised context-aware correlation filter for robust landmark tracking in liver ultrasound sequences |
| 264 | 1 | |c 2024 | |
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| 500 | |a Date Revised 14.02.2024 | ||
| 500 | |a published: Print-Electronic | ||
| 500 | |a Citation Status Publisher | ||
| 520 | |a © 2024 Walter de Gruyter GmbH, Berlin/Boston. | ||
| 520 | |a OBJECTIVES: Respiratory motion-induced displacement of internal organs poses a significant challenge in image-guided radiation therapy, particularly affecting liver landmark tracking accuracy | ||
| 520 | |a METHODS: Addressing this concern, we propose a self-supervised method for robust landmark tracking in long liver ultrasound sequences. Our approach leverages a Siamese-based context-aware correlation filter network, trained by using the consistency loss between forward tracking and back verification. By effectively utilizing both labeled and unlabeled liver ultrasound images, our model, Siam-CCF , mitigates the impact of speckle noise and artifacts on ultrasonic image tracking by a context-aware correlation filter. Additionally, a fusion strategy for template patch feature helps the tracker to obtain rich appearance information around the point-landmark | ||
| 520 | |a RESULTS: Siam-CCF achieves a mean tracking error of 0.79 ± 0.83 mm at a frame rate of 118.6 fps, exhibiting a superior speed-accuracy trade-off on the public MICCAI 2015 Challenge on Liver Ultrasound Tracking (CLUST2015) 2D dataset. This performance won the 5th place on the CLUST2015 2D point-landmark tracking task | ||
| 520 | |a CONCLUSIONS: Extensive experiments validate the effectiveness of our proposed approach, establishing it as one of the top-performing techniques on the CLUST2015 online leaderboard at the time of this submission | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a image-guided radiation therapy | |
| 650 | 4 | |a liver ultrasound landmark tracking | |
| 650 | 4 | |a respiratory motion estimation | |
| 650 | 4 | |a self-supervised context-aware correlation filter | |
| 700 | 1 | |a Wang, Junjie |e verfasserin |4 aut | |
| 700 | 1 | |a Gong, Shu |e verfasserin |4 aut | |
| 700 | 1 | |a Lan, Libin |e verfasserin |4 aut | |
| 700 | 1 | |a Geng, Li |e verfasserin |4 aut | |
| 700 | 1 | |a Wang, Siping |e verfasserin |4 aut | |
| 700 | 1 | |a Feng, Xin |e verfasserin |4 aut | |
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