BUSClean: Open-source software for breast ultrasound image pre-processing and knowledge extraction for medical AI
<|reference_start|>BUSClean: Open-source software for breast ultrasound image pre-processing and knowledge extraction for medical AI: Development of artificial intelligence (AI) for medical imaging demands curation and cleaning of large-scale clinical datasets comprising hundreds of thousands of images. Some modalities, such as mammography, contain highly standardized imaging. In contrast, breast ultrasound imaging (BUS) can contain many irregularities not indicated by scan metadata, such as enhanced scan modes, sonographer annotations, or additional views. We present an open-source software solution for automatically processing clinical BUS datasets. The algorithm performs BUS scan filtering, cleaning, and knowledge extraction from sonographer annotations. Its modular design enables users to adapt it to new settings. Experiments on an internal testing dataset of 430 clinical BUS images achieve >95% sensitivity and >98% specificity in detecting every type of text annotation, >98% sensitivity and specificity in detecting scans with blood flow highlighting, alternative scan modes, or invalid scans. A case study on a completely external, public dataset of BUS scans found that BUSClean identified text annotations and scans with blood flow highlighting with 88.6% and 90.9% sensitivity and 98.3% and 99.9% specificity, respectively. Adaptation of the lesion caliper detection method to account for a type of caliper specific to the case study demonstrates intended use of BUSClean in new data distributions and improved performance in lesion caliper detection from 43.3% and 93.3% out-of-the-box to 92.1% and 92.3% sensitivity and specificity, respectively. Source code, example notebooks, and sample data are available at https://github.com/hawaii-ai/bus-cleaning.<|reference_end|>
@article{bunnell2024busclean:,
    title={BUSClean: Open-source software for breast ultrasound image
  pre-processing and knowledge extraction for medical AI},
    author={Arianna Bunnell, Kailee Hung, John A. Shepherd, Peter Sadowski},
    journal={arXiv preprint arXiv:2407.11316},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.11316},
    primaryClass={eess.IV cs.CV}
}