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This study investigates a novel data augmentation approach for simulating surgical instruments, tools, and implants by image composition with transformed characters, numerals, and abstract symbols from open-source fonts. We analyse its suitability for the common spatial learning tasks of multi-label segmentation and anatomical landmark detection. The proposed technique is evaluated on 38 clinical intraoperative X-ray images with a high occurrence of objects overlaying the target anatomy. We demonstrate increased robustness towards superimposed surgical objects by incorporating our technique and provide an empirical rationale about the neglectable influence of realistic object shape and intensity information.
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