使用深度合奏对双视图X射线乳房图像注册的不确定性估计
William C Walton1,2, Seung-Jun Kim3
1University of Maryland, Baltimore County, CSEE Department, Baltimore, MD, 21250, USA.
Journal of imaging informatics in medicine
|September 23, 2024
概括
新的深度学习方法为记录乳腺癌病变的乳腺扫描视图提供不确定性估计. 这些信心评分有助于临床医生评估病变对应性,提高诊断准确性,特别是在密集的乳腺组织中.
科学领域:
- 医学成像分析分析 医学成像分析
- 放射学中的人工智能
- 机器学习用于诊断.
背景情况:
- 对于临床诊断来说,骨后 (CC) 和中侧斜 (MLO) 乳房扫描视图之间的精确病变对应是至关重要的.
- 自动注册工具可以帮助临床医生,但缺乏可靠的估计,限制了它们在具有挑战性的情况下的实用性,例如密集的乳腺组织.
- 基于卷积神经网络 (CNN) 的方法用于病变登记,但对其不确定性的量化对于临床信任至关重要.
研究的目的:
- 开发和评估不确定性估计技术,用于基于CNN的乳房造影病变登记.
- 通过提供多视图病变对应的信任度来增强自动注册工具的临床实用性.
- 通过帮助临床医生自信地识别和关联不同乳房镜视图中的病变来提高诊断能力.
主要方法:
- 实施基于深层集合的技术,使用负日志概率 (NLL) 成本函数来估计不确定性.
- 修改现有的CNN双视图损伤注册算法,使用三个不同的组合架构.
- 在合成,真实2D和真实3DX射线乳房学数据上评估不同组合尺寸和性能指标.
主要成果:
- 组合方法产生了与注册准确性相关的基于协差的不确定性圆.
- 圆尺寸为临床医生提供了对CC-MLO视图映射的可量化的信心指标.
- 不确定性估计有助于通过匹配病变检测和减少错误报警来改善计算机辅助检测 (CAD).
结论:
- 开发的不确定性估计技术显示出在乳房影像中临床应用的重大前景.
- 这些方法可以使临床医生能够自信地建立多视图病变对应,提高诊断准确度.
- 改善病变登记的信心有可能改进计算机辅助检测系统并减少诊断错误.
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