对跨域肋骨细分的透导向部分注释
Yuheng Yang1, Kun You2, Haoyang He1
1College of Information Engineering, Zhejiang University of Technology, Hangzhou, China.
概括
本研究介绍了透导向部分注释 (EGPA),这是一种用于医学成像中的肋骨细分的新型半监督方法. EGPA显著降低了注释成本,并改善了跨不同数据集的模型适应性,以最小的数据实现接近完全监督方法的性能.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 计算机视觉 计算机视觉
背景情况:
- 医学成像的深度学习,特别是肋骨细分,面临着挑战,因为专家注释的计算机断层扫描 (CT) 数据稀缺.
- 域移动限制了模型在不同临床数据集中的适用性,需要昂贵的重新注释以适应.
- 现有的半监督方法往往涉及样本级注释,导致冗余和高成本的完整CT扫描.
研究的目的:
- 提出一种新型的半监督方法,即以透为导向的部分注释 (EGPA),以减少肋骨细分的注释工作量.
- 通过智能选择信息区域进行注释,增强从零开始的模型培训和跨领域的适应性.
- 为了证明EGPA在实现高细分精度的有效性,而注释工作显著减少.
主要方法:
- 开发了以率为导向的部分注释 (EGPA) 方法,利用度指标来确定注释的最佳区域.
- 在EGPA框架内的综合对比学习,积极学习和自我培训策略.
- 在公共 (RibSegV2) 和私人胸部CT数据集上评估EGPA,用于肋骨细分.
主要成果:
- EGPA在源域获得了89.5%的Dice分数,在目标域获得了90.7%.
- 性能与完全监督模型 (89.9%和91.2%) 非常相匹配,分别仅使用了19%和18%的注释工作负载.
- 对初始培训和跨领域调整的注释成本和时间显著减少.
结论:
- EGPA大大减少了对肋骨细分的注释工作,使其更适合临床应用.
- 该方法有效地解决了领域转移的挑战,使得在不同的数据集中更容易部署细分工具.
- EGPA促进了高质量的注释数据集的创建,促进了临床环境中标准化和高效的肋骨分析.
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