Fully Automatic Data Labeling for Ultrasound Screen Detection
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866912943965208576 |
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| author | Gomez, Alberto Oliveira, Jorge Casero, Ramon Chartsias, Agis |
| author_facet | Gomez, Alberto Oliveira, Jorge Casero, Ramon Chartsias, Agis |
| contents | Ultrasound (US) machines display images on a built-in monitor, but routine transfer to hospital systems relies on DICOM. We propose a fully automatic method to generate labeled data that can be used to train a screen detector model, and a pipeline to use that model to extract and rectify the US image from a photograph of the monitor, without any need for human annotation. This removes the DICOM bottleneck and enables rapid testing and prototyping of new algorithms. In a proof-of-concept study, the rectified images retained enough visual fidelity to classify cardiac views with a balanced accuracy of 0.79 with respect to the native DICOMs., the rectified images retained enough visual fidelity to classify cardiac views with a balanced accuracy of 0.79 with respect to the native DICOMs. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_13197 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Fully Automatic Data Labeling for Ultrasound Screen Detection Gomez, Alberto Oliveira, Jorge Casero, Ramon Chartsias, Agis Computer Vision and Pattern Recognition Ultrasound (US) machines display images on a built-in monitor, but routine transfer to hospital systems relies on DICOM. We propose a fully automatic method to generate labeled data that can be used to train a screen detector model, and a pipeline to use that model to extract and rectify the US image from a photograph of the monitor, without any need for human annotation. This removes the DICOM bottleneck and enables rapid testing and prototyping of new algorithms. In a proof-of-concept study, the rectified images retained enough visual fidelity to classify cardiac views with a balanced accuracy of 0.79 with respect to the native DICOMs., the rectified images retained enough visual fidelity to classify cardiac views with a balanced accuracy of 0.79 with respect to the native DICOMs. |
| title | Fully Automatic Data Labeling for Ultrasound Screen Detection |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2511.13197 |