BUSClean：面向医学AI的乳腺超声图像预处理与知识提取开源软件
BUSClean: Open-source software for breast ultrasound image pre-processing and knowledge extraction for medical AI
AI总结：
针对乳腺超声影像存在大量元数据未标注异常的问题，研究提出开源软件BUSClean，可实现扫描过滤、清洗与知识提取，在内部与公开数据集上均取得优异性能，且支持灵活适配新数据分布。
摘要：
医学影像人工智能（AI）的开发需要对包含数十万张图像的大规模临床数据集进行整理与清洗。部分成像模态（如乳腺X线摄影）的影像具备高度标准化特征。相比之下，乳腺超声成像（BUS）可能存在大量扫描元数据未标注的异常内容，例如增强扫描模式、超声医师标注或额外视图。我们提出一款用于自动处理临床BUS数据集的开源软件方案。该算法可实现BUS扫描过滤（标记无效扫描与非B模式扫描）、清洗（双视图扫描检测、扫描区域裁剪及卡尺检测），以及从超声医师标注中提取知识（BI-RADS标注与测量字段）。其模块化设计支持用户适配新的应用场景。在包含430张临床BUS图像的内部测试数据集上的实验显示，该算法检测各类文本标注的灵敏度>95%、特异度>98%；检测带有血流高亮、替代扫描模式的扫描或无效扫描的灵敏度与特异度均>98%。在一个完全外部的公开BUS扫描数据集上开展的案例研究发现，BUSClean检测文本标注和血流高亮扫描的灵敏度分别为88.6%和90.9%，特异度分别为98.3%和99.9%。通过调整病灶卡尺检测方法以适配该案例研究中特有的一类卡尺，验证了BUSClean面向新数据分布的设计用途，且病灶卡尺检测的灵敏度与特异度从开箱即用的43.3%和93.3%分别提升至92.1%和92.3%。源代码、示例笔记本与样本数据可在https://github.com/hawaii-ai/bus-cleaning获取。
英文摘要：
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.
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