US-DICOMizer

US-DICOMizer is an advanced application designed to automate and streamline the preparation of ultrasound diagnostic DICOM files for AI-based workflows. The application incorporates three key functionalities:

1. anonymisation to remove sensitive patient information while preserving essential metadata for AI tasks,
2. cropping to extract relevant regions from images or videos, and
3. tagging to annotate files with critical metadata, such as anatomical position, imaging purpose, and other contextual information.

These functionalities aim to address the unique requirements of ultrasound imaging data preparation while ensuring compliance with data privacy regulations and clinical standards.

Changelog

Changes in version 5.2

Updated manual/documentation.

Changes in version 5.1

New utility modules for DICOM and file handling:

Added anonymized_filename_utils.py with comprehensive functions for encoding/decoding anonymized filenames, mapping thrombosis and compressibility codes, and parsing filenames into structured data.
Introduced autocrop_utils.py, providing functions to detect and combine crop boxes for DICOM images using threshold, mode, edge, and hybrid methods, and reading region boxes from DICOM metadata.
Added package_io_utils.py to handle sidecar annotation file path resolution, normalization, and discovery, improving annotation file management in DICOM packages.

Build and packaging improvements:

Updated the build workflow (.github/workflows/build.yml) and build.ps1 to generate and include a RELEASE_DATE file alongside the VERSION file, ensuring release date metadata is packaged with the build.
Added the RELEASE_DATE file with the current release date.

Testing:

Added tests/test_anonymized_filename_utils.py with extensive unit tests covering filename parsing, building, and code/label conversions for the new utilities.

Changes in version 5.0

Added annotation tools and refactored canvas resizing.
Implemented version management and updates.

Changes in version 4.16

Added auto detection for cropping
Minor changes at logs output

Changes in version 4.15

Bug fix in load_tags()
All DICOM attributes load instantly
Change title in the 1st header of the treeview to Group,Element

Bug fix when convert_all_to_jpeg == "yes"
New compressed JPEG file save with the rigth Transfer Syntax JPEG Baseline (Process 1)
and Photometric Interpretation YBR_FULL_422

Change the timestamp date - time format
from d-m-y HH:MM:SS 12h format
to y-m-d HH:MM:SS 24h format

Changes in version 4.14

Patient's ID not delete but define as the filename
The values for X0, Y0, X1, Y1 not delete but update them
Add the About window
Removed the free text area

Changes in version 4.13

If in [settings] section at settings.ini file
user_can_change_compression_level = no
then at settings window the jpeg quality text entry is disabled

Added the free text area at the footer
Minor updates at footer section

Changes in version 4.12

Smaller font size in treeviews
Separation of attributes into metadata and dataset
Added ability to copy values ​​from treeview with attribute tags by right-click
Added options (title) to filedialog
Removed simpleTK and scikit-image modules as they are no longer used

Citation

If you use US-DICOMizer in a scientific publication, we would appreciate using the following citation:

• Pechlivanis, D., Didaskalou, S., Kaldoudi, E. and Drosatos, G. (2025). Preparing Ultrasound Imaging Data for Artificial Intelligence Tasks: Anonymisation, Cropping, and Tagging. In Proceedings of the 18th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2025) - Volume 2: HEALTHINF, ISBN 978-989-758-731-3, ISSN 2184-4305, pages 951-958. DOI: 10.5220/0013379400003911

and as BibTeX:

@incollection{Pechlivanis_US-DICOMizer_2025,
    author       = {Pechlivanis, Dimitrios and Didaskalou, Stylianos and Kaldoudi, Eleni and Drosatos, George},
    title        = {Preparing Ultrasound Imaging Data for Artificial Intelligence Tasks: Anonymisation, Cropping, and Tagging},
    keywords     = {Ultrasound Imaging;DICOM;Anonymisation;Cropping;Tagging;Artificial Intelligence (AI)},
    booktitle    = {Proceedings of the 18th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2025) - Volume 2: HEALTHINF},
    volume       = {2},
    year         = {2025},
    pages        = {951-958},
    publisher    = {SciTePress},
    organization = {INSTICC},
    doi          = {10.5220/0013379400003911},
    isbn         = {978-989-758-731-3},
    issn         = {2184-4305}
}