基于Noise2Void的无监督消噪方法在数字造乳镜中的评估
Francesca Angelone1, Alessandra Franco2, Alfonso Maria Ponsiglione3
1Department of Engineering, University of Sannio, Corso Garibaldi 107, Benevento, 82100, Italy.
Scientific reports
|October 13, 2025
概括
无监督深度学习方法Noise2Void (N2V) 能够有效地否定数字乳房图像,而不需要清洁的示例. 这种方法有望提高乳腺癌查质量,特别是在低剂量成像方案中.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 放射学 放射学是一门学科.
背景情况:
- 全场数字造乳镜 (FFDM) 对于乳腺癌查至关重要,但会受到图像噪声的影响.
- 传统的消噪方法在FFDM中难以应对复杂的噪音.
- 监督深度学习需要广泛的配对噪音/无噪音数据,通常无法用于乳房镜.
研究的目的:
- 调查Noise2Void (N2V) 的有效性,这是一个无监督的无声化方法,用于数字造乳镜图像.
- 为了评估N2V在改善图像质量的表现,而不需要地面真相无声图像.
- 评估N2V在回顾性和低剂量乳房造影中临床应用的潜力.
主要方法:
- 调整并将无监督深度学习方法Noise2Void (N2V) 应用到FFDM图像上.
- 使用盲点掩盖,N2V技术,以消除没有干净对应的图像.
- 使用定量指标 (CNR,SSIM) 和放射科医生对真实和模拟噪音图像的定性评估来评估性能.
主要成果:
- N2V的应用导致了对被剥离的乳房造影图像的评估指标 (CNR,SSIM) 的增加.
- 发现N2V的定量性能与传统的无雾化方法可比.
- 质量评估表明N2V的临床使用潜力,提供了一个灵活的,无注释的解决方案.
结论:
- Noise2Void是一种可行的无监督方法,用于消除数字造乳镜的噪音,其结果与传统技术相美.
- N2V提供了一种灵活的,无注释的方法,适用于回顾性分析和低剂量乳腺学.
- 这种无监督的方法有可能通过提高图像质量而提高乳腺癌查,而无需大量的数据要求.
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