YoloCurvSeg:你只标记一个的骨架,用于船型曲线结构细分的曲线结构
Li Lin1, Linkai Peng2, Huaqing He3
1Department of Electronic and Electrical Engineering, Southern University of Science and Technology, Shenzhen, China; Department of Electrical and Electronic Engineering, the University of Hong Kong, Hong Kong, China; Jiaxing Research Institute, Southern University of Science and Technology, Jiaxing, China.
Medical image analysis
|September 6, 2023
概括
本研究介绍了YoloCurvSeg,这是一个新的弱监督的学习框架,用于对船只等曲线结构进行细分. 它使用最小的注释实现了超过97%的完全监督性能,显著推进了医疗图像分析.
科学领域:
- 计算机视觉 计算机视觉
- 医学图像分析 医学图像分析
- 机器学习 机器学习
背景情况:
- 弱监督学习 (WSL) 为图像细分中的高注释成本提供了解决方案.
- 现有的WSL方法在曲线结构和有限的数据方面扎.
- 曲线结构细分 (例如血管,神经) 在医学成像中至关重要.
研究的目的:
- 为曲线结构开发一个新的WSL细分框架.
- 为了应对有限的监督和细分中的非星形状的挑战.
- 为了实现精确的细分与最小的注释.
主要方法:
- 建议使用图像合成的YoloCurvSeg框架.
- 通过 inpainting 扩展骨来生成背景.
- 使用空间殖民算法和对比学习合成器进行前景生成.
- 用合成和未标记的数据训练一个细分器.
主要成果:
- 在四个数据集上,YoloCurvSeg显著优于最先进的WSL方法.
- 实现了>97%的完全监督性表现,注释最小 (0.03%-1.40%).
- 在OCTA500,CORN,DRIVE和CHASEDB1数据集上证明了有效性.
结论:
- YoloCurvSeg有效地使用稀疏注释对曲线结构进行细分.
- 图像合成方法克服了现有的WSL方法的局限性.
- 这种框架大大减少了注释工作,同时保持了高细分精度.
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