Authors:
Dimitrios Pechlivanis
1
;
Stylianos Didaskalou
2
;
Eleni Kaldoudi
1
;
2
and
George Drosatos
2
Affiliations:
1
School of Medicine, Democritus University of Thrace, 68100 Alexandroupoli, Greece
;
2
Institute for Language and Speech Processing, Athena Research Center, 67100 Xanthi, Greece
Keyword(s):
Ultrasound Imaging, DICOM, Anonymisation, Cropping, Tagging, Artificial Intelligence (AI).
Abstract:
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 int
o 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.
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