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适应边界增强的子损失用于图像分割
Yanyan Zheng1, Bihan Tian2,3, Shuchen Yu2,3
1Department of Neurology, Wenzhou People's Hospital, The Third Affiliated Hospital of Shanghai University, Wenzhou 325041, China.
Biomedical signal processing and control
|March 10, 2025
概括
一个新的自适应边界增强的Dice (ABeDice) 损失函数通过增强边界检测来改善医疗图像细分. 与传统方法相比,这种深度学习方法提供了更高的准确性和更快的融合.
科学领域:
- 医学图像分析 医学图像分析
- 深度学习 (Deep Learning) 是一种深度学习.
- 计算机视觉 计算机视觉
背景情况:
- 深度学习模型对于医学图像细分至关重要.
- 损失函数的选择显著影响了细分性能.
- 准确的细分依赖于精确的边界定位.
研究的目的:
- 引入和评估一个自适应边界增强的 Dice (ABeDice) 损失函数.
- 为了提高医疗图像分割的准确性和效率.
- 为了提高对象边界的检测和定位.
主要方法:
- 通过将指数递归互补 (ERC) 函数与 Dice 损失集成,开发了 ABeDice 损失函数.
- 利用ERC函数来利用像素预测概率进行边界增强.
- 采用预测概率的动态调整来优先考虑更高的值.
- 在公共数据集上使用Swin-Unet架构验证了ABeDice损失 (REFUGE,ISIC2018,RIT-Eyes).
主要成果:
- 在ABeDice的损失实现了高的平均子相似系数:0.9114 (REFUGE),0.8940 (ISIC2018),和0.9418 (RIT-Eyes).
- 与传统的子损失及其变体 (通用子,Tervkey,灵敏度-特异性) 相比,表现出卓越的性能.
- 由于适应性概率分布,表现出更好的量子化潜力和趋同率.
结论:
- 该ABeDice损失功能有效地提高了医疗图像细分的准确性.
- 它改善了边界检测和本地化,导致了更好的整体细分.
- 拟议的方法为基于深度学习的医学图像细分任务提供了有前途的进展.
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