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使用深度学习来改善MRI上对乳腺癌的非系统性观察

Sarah Eskreis-Winkler¹, Natsuko Onishi^1,2, Katja Pinker¹

¹Memorial Sloan Kettering Cancer Center, Department of Radiology, New York, NY.

Journal of breast imaging

|March 1, 2024

概括

深度学习在乳房MRI扫描中准确识别含有瘤的切片,提高了放射科医生的效率. 这种人工智能工具可以帮助绕过堆叠图像的手动滚动,帮助在瘤董事会会议期间更快地审查.

科学领域:

医疗成像医学成像
人工智能的人工智能
在瘤学瘤学.

背景情况:

乳腺MRI对于癌症的检测和分期至关重要.
手动审查堆叠的乳房MRI切片可能是耗时的.
人工智能为优化图像分析工作流提供了潜在的解决方案.

研究的目的:

评估深度学习的可行性,以识别乳房MRI中含有瘤的轴切片.
为了评估卷积神经网络 (CNN) 对此任务的准确性.
量化放射科医生可能节省的时间.

主要方法:

对来自侵袭性乳腺癌患者的273个乳房MRI扫描进行了回顾性分析.
开发和培训CNN,将子图像分类为"癌症"或"没有癌症".
对算法的准确性,灵敏性和特异性与病理学结果的验证.

主要成果:

深度学习系统在一组测试中实现了92.8%的瘤检测准确度.
敏感度为89.5%,特异性为94.3%.
在没有人工智能协助的情况下,手动滚动每次需要3-45秒.

结论:

深度学习有效地识别了乳房MRI中含有瘤的切片.
这项技术可以集成到PACS中,以简化图像审查.
潜在的好处包括减少阅读时间和提高效率,特别是在多学科团队会议期间.

关键词:

人工智能的人工智能是人工智能.乳房核磁共振成像 (MRI) 进行.乳腺癌乳腺癌乳腺癌卷积神经网络是一种卷积神经网络.深度学习是一种深度学习.瘤检测瘤检测瘤检测

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Magnetic Resonance Imaging

Magnetic Resonance Imaging

Magnetic resonance imaging (MRI) is a noninvasive medical imaging technique based on a phenomenon of nuclear physics discovered in the 1930s, in which matter exposed to magnetic fields and radio waves was found to emit radio signals. In 1970, a physician and researcher named Raymond Damadian noticed that malignant (cancerous) tissue gave off different signals than normal body tissue. He applied for a patent for the first MRI scanning device in clinical use by the early 1980s. The early MRI...