BUSClean: Open-source software for breast ultrasound image pre-processing and knowledge extraction for medical AI

TL;DR

BUSClean is an open-source tool that automates preprocessing and knowledge extraction from breast ultrasound images, improving dataset quality for AI development with high accuracy and adaptability.

Contribution

This paper introduces BUSClean, a modular open-source software for cleaning and annotating breast ultrasound datasets, with demonstrated high accuracy and adaptability to new data.

Findings

Achieved over 95% sensitivity and 98% specificity in detecting annotations and scan irregularities.

Successfully adapted caliper detection to new data, significantly improving sensitivity and specificity.

Demonstrated robustness across internal and external datasets.

Abstract

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 (flagging of invalid and non-B-mode scans), cleaning (dual-view scan detection, scan area cropping, and caliper detection), and knowledge extraction (BI-RADS Labeling and Measurement fields) 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 the 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.