Preparing Ultrasound Imaging Data for Artificial Intelligence Tasks: Anonymisation, Cropping, and Tagging

Ultrasound imaging is a widely used diagnostic method in various clinical contexts, requiring efficient and
accurate data preparation workflows for artificial intelligence (AI) tasks. Preparing ultrasound data presents
challenges such as ensuring data privacy, extracting diagnostically relevant regions, and associating contextual
metadata. This paper introduces a standalone application designed to streamline the preparation of ultrasound
DICOM files for AI applications across different medical use cases. The application facilitates three key processes:
(1) anonymisation, ensuring compliance with privacy standards by removing sensitive metadata; (2)
cropping, isolating relevant regions in images or video frames to enhance the utility for AI analysis; and (3)
tagging, enriching files with additional metadata such as anatomical position and imaging purpose. Built with
an intuitive interface and robust backend, the application optimises DICOM file processing for efficient integration
into AI workflows. The effectiveness of the tool is evaluated using a dataset of Deep Vein Thrombosis
(DVT) ultrasound images, demonstrating significant improvements in data preparation efficiency. This work
establishes a generalizable framework for ultrasound imaging data preparation while offering specific insights
into DVT-focused AI workflows. Future work will focus on further automation and expanding support to additional
imaging modalities as well as evaluating the tool in a clinical setting.