Self-Supervised Ultrasound Screen Detection
This addresses a domain-specific bottleneck in medical imaging for clinicians and researchers by enabling rapid testing and prototyping without DICOM reliance.
The paper tackled the problem of extracting ultrasound images from photographs of monitor screens to bypass DICOM transfer bottlenecks, achieving a balanced accuracy of 0.79 for cardiac view classification compared to native DICOMs.
Ultrasound (US) machines display images on a built-in monitor, but routine transfer to hospital systems relies on DICOM. We propose a self-supervised pipeline to extract the US image from a photograph of the monitor. 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.