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Accurately annotated ultrasonic images are vital components of a high-qualitymedical report. Hospitals often have strict guidelines on the types ofannotations that should appear on imaging results. However, manually inspectingthese images can be a cumbersome task. While a neural network could potentiallyautomate the process, training such a model typically requires a dataset ofpaired input and target images, which in turn involves significant humanlabour. This study introduces an automated approach for detecting annotationsin images. This is achieved by treating the annotations as noise, creating aself-supervised pretext task and using a model trained under the Noise2Noisescheme to restore the image to a clean state. We tested a variety of modelstructures on the denoising task against different types of annotation,including body marker annotation, radial line annotation, etc. Our resultsdemonstrate that most models trained under the Noise2Noise scheme outperformedtheir counterparts trained with noisy-clean data pairs. The costumed U-Netyielded the most optimal outcome on the body marker annotation dataset, withhigh scores on segmentation precision and reconstruction similarity. Wereleased our code at https://github.com/GrandArth/UltrasonicImage-N2N-Approach.

Ultrasonic Image's Annotation Removal: A Self-supervised Noise2Noise Approach