交叉扫描融合网络:一个基于注册的框架,用于在腰部疼痛评估中进行注释效率高的3D超声波细分
Pengyu Chen1,2, Zixue Zeng2,3, Xiaoyan Zhao2
1Department of Computer Science, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.
Medical physics
|January 30, 2026
概括
新的交叉扫描融合网络 (CSFN) 准确地为慢性腰部疼痛 (cLBP) 研究细分3D超声波图像. 这种方法显著改善了解剖层细分与最小的手动注释,加速临床分析.
科学领域:
- 医学成像医学成像
- 人工智能的人工智能是人工智能.
- 生物医学工程 生物医学工程
背景情况:
- 慢性腰部疼痛 (cLBP) 显著影响生活质量,但其机制尚不清楚.
- 3D超声波成像为cLBP提供了洞察力,但由于数据量和图像质量,面临人工注释挑战.
研究的目的:
- 开发和验证交叉扫描融合网络 (CSFN) 以在3D超声图像中对解剖组织层进行细分.
- 提高cLBP研究中3D超声波数据的注释效率.
主要方法:
- 使用了腰部3DB模式超声波卷,并使用了6个标注的组织层.
- 开发了CSFN,一个VoxelMorph风格的弹性注册网络,具有预测的豪斯多夫距离损失 (PHDL).
- 实施样本效率 (CSFN-SEL) 和半监督 (CSFN-SSL) 学习策略,包括用于增强数据的数据合成.
主要成果:
- CSFN-SEL的表现优于完全监督的nnU-Net,平均子系数提高了5.28% (p < 0.05/3).
- 在只有5个和19个训练样本的情况下,CSFN-SSL实现了79.33% (±1.96%) 和81.14% (±1.43%) 的细分精度.
- 在CSFN-SSL中,仅使用五个标记扫描 (效果大小d = 0.671,p < 0.05/3),以最小的注释显示了9.51%的性能增长.
结论:
- 在3D超声波图像中,CSFN显著提高了细分精度和稳定性,即使具有有限的标记数据.
- 该CSFN方法为推进cLBP成像和分析提供了一个实际的解决方案.
- 使用CSFN的自动细分可以加速cLBP的临床评估和管理.
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