自动标注超声屏图像，无需人工干预即可训练检测模型。
Fully Automatic Data Labeling for Ultrasound Screen Detection
- 用照片自动生成带标签的超声图像数据，免去人工标注。
- 修复后的图像可准确识别心脏视图，准确率达79%。
- 适合快速测试新算法，突破DICOM传输瓶颈。
超声设备在内置显示器上显示图像，但常规传输至医院系统依赖DICOM。本文提出一种全自动方法，生成可用于训练屏幕检测模型的标注数据，并构建一个无需人工标注的流水线，从显示器照片中提取并校正超声图像。该方法摆脱了DICOM限制，支持新算法的快速测试与原型开发。概念验证研究显示，校正后的图像保留足够视觉保真度，用于心脏视图分类时，平衡准确率达0.79，与原始DICOM相当。
原文摘要 · Abstract (English)
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.
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