丢失的DICOM转换可能会影响AI性能
Robin Sebastian Mayer1, Fabian Fliedner1, Ingvild Frøberg Mathisen1
1Dr. Senckenberg Institutes of Pathology and Human Genetics, Goethe-University Frankfurt, University Hospital Frankfurt, Frankfurt am Main, Germany.
Scientific reports
|July 8, 2025
概括
将病理学幻灯片数字化为DICOM格式的长期存储至关重要. 虽然转换的图像看起来很相似,但它们并不完全相同,这会影响人工智能模型的性能,并需要重新评估用于诊断.
科学领域:
- 数字病理学数字病理学
- 医学成像标准 医学成像标准
- 医疗保健中的人工智能
背景情况:
- 病理学幻灯片越来越多地被数字化,以提高可访问性.
- 将数字化幻灯片存储在DICOM格式中可以确保长期的可访问性.
- 当前的数字化工作流通常涉及特定供应商格式和转换到DICOM,这引发了对数据完整性的担忧.
研究的目的:
- 为了研究将MRXS病理幻灯片图像转换为DICOM格式的后果.
- 评估DICOM转换对图像数据和随后的AI模型性能的影响.
- 评估DICOM适合用于诊断用途和未来的互操作性.
主要方法:
- 膀,卵巢和前列腺组织的MRXS文件使用供应商转换器和具有JPEG再压缩的开源工具将其转换为DICOM.
- 人类视觉检查和结构相似度指数 (SSIM) 用于比较原始和转换的图像.
- 人工智能 (AI) 模型,包括卷积神经网络 (CNN) 和基础模型,用于区分图像格式和评估性能变化.
主要成果:
- 在原始和转换的DICOM图像之间没有观察到人类可感知的差异.
- 转换的图像与原始图像不相同,平均SSIM分数在0.85到0.96.9之间.
- 人工智能模型可以以高达99.5%的准确度区分原始和转换的图像.
- 在64种情景中,在5种情景中发现了显著的人工智能性能差异,特别是在有限的培训数据的情况下.
- 供应商特定的转换器通常实现更高的SSIM值.
结论:
- 病理幻灯片的DICOM转换并不是无损的,并且转换的图像与原始图像不相同.
- 所有的诊断过程和人工智能算法必须使用DICOM转换的文件进行重新评估.
- 尽管有转换差异,但DICOM格式为未来的应用提供了出色的互操作性.
- 使用转换文件进行初始人工智能训练并不总是显示性能下降,这表明未来使用的潜力.
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