Measuring proximity to standard planes during fetal brain ultrasound scanning
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arXiv
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| Auteurs principaux: | , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866911834902102016 |
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| author | Di Vece, Chiara Cirigliano, Antonio Lous, Meala Le Napolitano, Raffaele David, Anna L. Peebles, Donald Jannin, Pierre Vasconcelos, Francisco Stoyanov, Danail |
| author_facet | Di Vece, Chiara Cirigliano, Antonio Lous, Meala Le Napolitano, Raffaele David, Anna L. Peebles, Donald Jannin, Pierre Vasconcelos, Francisco Stoyanov, Danail |
| contents | This paper introduces a novel pipeline designed to bring ultrasound (US) plane pose estimation closer to clinical use for more effective navigation to the standard planes (SPs) in the fetal brain. We propose a semi-supervised segmentation model utilizing both labeled SPs and unlabeled 3D US volume slices. Our model enables reliable segmentation across a diverse set of fetal brain images. Furthermore, the model incorporates a classification mechanism to identify the fetal brain precisely. Our model not only filters out frames lacking the brain but also generates masks for those containing it, enhancing the relevance of plane pose regression in clinical settings. We focus on fetal brain navigation from 2D ultrasound (US) video analysis and combine this model with a US plane pose regression network to provide sensorless proximity detection to SPs and non-SPs planes; we emphasize the importance of proximity detection to SPs for guiding sonographers, offering a substantial advantage over traditional methods by allowing earlier and more precise adjustments during scanning. We demonstrate the practical applicability of our approach through validation on real fetal scan videos obtained from sonographers of varying expertise levels. Our findings demonstrate the potential of our approach to complement existing fetal US technologies and advance prenatal diagnostic practices. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_07124 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Measuring proximity to standard planes during fetal brain ultrasound scanning Di Vece, Chiara Cirigliano, Antonio Lous, Meala Le Napolitano, Raffaele David, Anna L. Peebles, Donald Jannin, Pierre Vasconcelos, Francisco Stoyanov, Danail Computer Vision and Pattern Recognition Artificial Intelligence I.2.0; I.4.0; J.2.0; J.3.0 This paper introduces a novel pipeline designed to bring ultrasound (US) plane pose estimation closer to clinical use for more effective navigation to the standard planes (SPs) in the fetal brain. We propose a semi-supervised segmentation model utilizing both labeled SPs and unlabeled 3D US volume slices. Our model enables reliable segmentation across a diverse set of fetal brain images. Furthermore, the model incorporates a classification mechanism to identify the fetal brain precisely. Our model not only filters out frames lacking the brain but also generates masks for those containing it, enhancing the relevance of plane pose regression in clinical settings. We focus on fetal brain navigation from 2D ultrasound (US) video analysis and combine this model with a US plane pose regression network to provide sensorless proximity detection to SPs and non-SPs planes; we emphasize the importance of proximity detection to SPs for guiding sonographers, offering a substantial advantage over traditional methods by allowing earlier and more precise adjustments during scanning. We demonstrate the practical applicability of our approach through validation on real fetal scan videos obtained from sonographers of varying expertise levels. Our findings demonstrate the potential of our approach to complement existing fetal US technologies and advance prenatal diagnostic practices. |
| title | Measuring proximity to standard planes during fetal brain ultrasound scanning |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence I.2.0; I.4.0; J.2.0; J.3.0 |
| url | https://arxiv.org/abs/2404.07124 |