ULS4US:用于2D超声波图像的通用病变细分框架
Xinglong Wu1, Yan Jiang1, Hanshuo Xing1
1School of Computer Science and Technology, Hubei Key Laboratory of Intelligent Robot, Wuhan Institute of Technology, People's Republic of China.
Physics in medicine and biology
|June 21, 2023
概括
一个新的深度学习框架,ULS4US,可以在各种器官的超声图像中实现通用病变细分. 这种方法提高了诊断的准确性,并减少了临床医生在计算机辅助诊断中的工作量.
科学领域:
- 医学成像和人工智能 医学成像和人工智能
- 用于医学图像分析的深度学习.
- 超声波图像细分 超声波图像细分
背景情况:
- 目前用于超声波 (美国) 图像细分的深度学习 (DL) 方法是器官特定的,缺乏计算机辅助诊断的通用框架.
- 现有的DL模型仅限于乳腺或甲状腺等特定器官,阻碍了更广泛的临床应用.
- 需要一个通用的框架来对美国图像中各种器官的病变进行细分.
研究的目的:
- 开发一种适用于不同器官的超声波图像 (ULS4US) 的通用病变细分框架.
- 解决计算机辅助诊断场景中对通用工具的需求.
- 为了利用独特的超声波回声强度和病变模式进行细分.
主要方法:
- 提出了一个ULS4US框架,包含一个多进多出 (MIMO) UNet,用于多尺度的特征提取.
- 实施了两阶段的损伤意识学习算法,用于递归的损伤本地化和细分.
- 引入了损伤适应性损失函数,用于网络输出的加权和自我监督组件.
主要成果:
- 与统一数据集上的六个最先进的模型相比,ULS4US实现了更高的性能 (精度:0.956,DSC:0.836,HD:7.849,mIoU:0.731).
- 统一的数据集包括来自公共和私人来源的三种器官类型的2200多张图像.
- 对比实验表明了ULS4US的可扩展性,并提供了额外的数据.
结论:
- 这项研究表明了临床超声波中基于DL的普遍病变细分的潜力.
- ULS4US可以显著减少临床医生的工作量.
- 该框架有可能提高医学成像中的诊断准确性.
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