Motion Informed Needle Segmentation in Ultrasound Images
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arXiv
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| Hauptverfasser: | , , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2023
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| _version_ | 1866908562242928640 |
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| author | Goel, Raghavv Morales, Cecilia Singh, Manpreet Dubrawski, Artur Galeotti, John Choset, Howie |
| author_facet | Goel, Raghavv Morales, Cecilia Singh, Manpreet Dubrawski, Artur Galeotti, John Choset, Howie |
| contents | Segmenting a moving needle in ultrasound images is challenging due to the presence of artifacts, noise, and needle occlusion. This task becomes even more demanding in scenarios where data availability is limited. In this paper, we present a novel approach for needle segmentation for 2D ultrasound that combines classical Kalman Filter (KF) techniques with data-driven learning, incorporating both needle features and needle motion. Our method offers three key contributions. First, we propose a compatible framework that seamlessly integrates into commonly used encoder-decoder style architectures. Second, we demonstrate superior performance compared to recent state-of-the-art needle segmentation models using our novel convolutional neural network (CNN) based KF-inspired block, achieving a 15\% reduction in pixel-wise needle tip error and an 8\% reduction in length error. Third, to our knowledge we are the first to implement a learnable filter to incorporate non-linear needle motion for improving needle segmentation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2312_01239 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Motion Informed Needle Segmentation in Ultrasound Images Goel, Raghavv Morales, Cecilia Singh, Manpreet Dubrawski, Artur Galeotti, John Choset, Howie Image and Video Processing Computer Vision and Pattern Recognition Machine Learning Robotics Segmenting a moving needle in ultrasound images is challenging due to the presence of artifacts, noise, and needle occlusion. This task becomes even more demanding in scenarios where data availability is limited. In this paper, we present a novel approach for needle segmentation for 2D ultrasound that combines classical Kalman Filter (KF) techniques with data-driven learning, incorporating both needle features and needle motion. Our method offers three key contributions. First, we propose a compatible framework that seamlessly integrates into commonly used encoder-decoder style architectures. Second, we demonstrate superior performance compared to recent state-of-the-art needle segmentation models using our novel convolutional neural network (CNN) based KF-inspired block, achieving a 15\% reduction in pixel-wise needle tip error and an 8\% reduction in length error. Third, to our knowledge we are the first to implement a learnable filter to incorporate non-linear needle motion for improving needle segmentation. |
| title | Motion Informed Needle Segmentation in Ultrasound Images |
| topic | Image and Video Processing Computer Vision and Pattern Recognition Machine Learning Robotics |
| url | https://arxiv.org/abs/2312.01239 |