BUSClean:开源软件用于乳房超声波图像预处理和医学AI知识提取
Arianna Bunnell1,2, Kailee Hung1, John A Shepherd2
1Department of Information and Computer Sciences, University of Hawai'i at Mānoa, Honolulu, HI, United States of America.
PloS one
|December 11, 2024
概括
本研究介绍了BUSClean,这是一个开源软件,用于自动处理乳房超声波成像 (BUS) 数据集. BUSClean通过过,清理和从复杂的BUS数据中提取知识来增强AI开发,提高准确性和效率.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 软件工程 软件工程 软件工程
背景情况:
- 医疗成像的人工智能 (AI) 开发需要广泛的,高质量的临床数据集.
- 乳房超声波成像 (BUS) 数据集由于固有的不规则性和非标准化成像实践而存在独特的挑战.
- 现有的数据处理方法不足以应对BUS数据的复杂性.
研究的目的:
- 开发和验证一个开源软件解决方案,用于自动处理临床乳腺超声波成像 (BUS) 数据集.
- 提高为AI模型培训准备BUS数据的效率和准确性.
- 解决BUS.中非标准化的图像和注释所带来的挑战.
主要方法:
- 开发了一个名为BUSClean的模块化,开源软件工具.
- 实现用于BUS扫描过 (无效/非B模式扫描),清理 (双视图检测,裁剪,校准检测) 和知识提取 (BI-RADS,测量) 的算法.
- 通过对内部和外部公共公共公共汽车数据集的实验验证,包括针对特定型的调整.
主要成果:
- 在检测各种文本注释和扫描内部数据集上的不规则方面,BUSClean实现了>95%的灵敏度和>98%的特异性.
- 对外部数据集的性能显示出高灵敏度 (88.6%对于文本,90.9%对于血流) 和特异性 (>98%).
- 修改了病变标杆检测,性能从43.3%提高到92.1%的灵敏度,特异性从93.3%提高到92.3%.
结论:
- BUSClean提供了一个强大的,适应性的解决方案,用于对临床BUS数据集的自动处理.
- 该软件显著提高了用于AI开发的BUS数据的质量和可用性.
- 开源性质和模块化设计为各种研究环境提供了更广泛的采用和定制.
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