End-to-End Restoration Versus Segmentation-Guided Inpainting for Ultrasound Mark Removal

Annotation marks in ultrasound images can obscure diagnostically relevant features and introduce bias into automated analysis pipelines. In this study, we systematically compare two annotation suppression techniques: end-to-end image restoration frameworks and a two-stage segmentation-guided inpainting approach. Representative models from both techniques are evaluated on a curated dataset of nearly 5,000 images from 95 patients, containing both synthetically applied and physician-generated annotation marks. Experimental results demonstrate that while end-to-end enhancement methods perform favorably in removing synthetic annotations, the segmentation-guided inpainting system achieves superior performance on physician-generated marks, effectively preserving structural and diagnostic details. These findings highlight the robustness of segmentation-guided inpainting and its potential as a reliable solution for mitigating annotation artifacts, thereby improving the accuracy and usability of computer-aided ultrasound analysis.