Summary of Measuring Proximity to Standard Planes During Fetal Brain Ultrasound Scanning, by Chiara Di Vece et al.
Measuring proximity to standard planes during fetal brain ultrasound scanning
by Chiara Di Vece, Antonio Cirigliano, Meala Le Lous, Raffaele Napolitano, Anna L. David, Donald Peebles, Pierre Jannin, Francisco Vasconcelos, Danail Stoyanov
First submitted to arxiv on: 10 Apr 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel pipeline for ultrasound plane pose estimation is introduced, aiming to improve navigation to standard planes in the fetal brain for more effective diagnosis. The semi-supervised segmentation model utilizes labeled SPs and unlabeled 3D US volume slices, enabling reliable segmentation across diverse fetal brain images. Additionally, the model incorporates a classification mechanism to identify the fetal brain precisely. By filtering out frames lacking the brain and generating masks for those containing it, the pipeline enhances relevance in clinical settings. The approach combines fetal brain navigation from 2D ultrasound video analysis with a US plane pose regression network, providing sensorless proximity detection to SPs and non-SPs planes. Proximity detection to SPs is emphasized as essential for guiding sonographers, offering an advantage over traditional methods by allowing earlier and more precise adjustments during scanning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new way to use ultrasound images to better navigate the fetal brain. It’s like having a special tool that helps doctors take clearer pictures of the baby’s brain. The tool uses both labeled and unlabeled images to make it work, and it can even figure out which parts of the image have the brain in them. This makes it easier for sonographers to adjust their tools during scans, giving them a better chance at getting clear pictures. |
Keywords
» Artificial intelligence » Classification » Pose estimation » Regression » Semi supervised
