对于体积医学图像细分的边界混
Xin You1, Ming Ding2, Minghui Zhang1
1Institute of Medical Robotics, Shanghai Key Laboratory of Flexible Medical Robotics, Tongren Hospital, Shanghai Jiao Tong University, Shanghai, China; Institute of Imaging Processing and Pattern Recognition, Shanghai Jiao Tong University, Shanghai, China.
Medical image analysis
|February 3, 2026
概括
本研究介绍了PP-Net用于准确的体积图像细分,解决边界混. 新的推和拉互动增强了边界表示,以改善医疗图像分析.
科学领域:
- 医疗成像医学成像
- 计算机视觉 计算机视觉
- 图像细分 图像细分
背景情况:
- 在体积图像中精确的边界细分对于医学诊断和干预至关重要.
- 现有的方法与边界混作斗争,并忽略了边界区域相互作用.
研究的目的:
- 通过解决边界混,提出一个新型网络 (PP-Net) 进行准确的体积图像细分.
- 使用新的相互作用机制,模拟混乱的边界区域的形状特征.
主要方法:
- 介绍了推进和拉动的相互作用,以重新构想边界生成.
- 开发了带有语义差异模块 (SDM) 和类集群模块 (CCM) 的PP-Net.
- SDM将边界推向地面真相;CCM延伸交叉的边界.
主要成果:
- 在多个数据集上,PP-Net在现有细分网络上表现出优越的性能.
- 观察到显著的改善,特别是在豪斯多夫距离和平均对称表面距离指标.
- SDM和CCM模块增强了基线U形和基于SAM的模型.
结论:
- PP-Net有效地将混乱的边界区域模拟为体积图像.
- 推拉相互作用机制为边界细分提供了一种新的方法.
- 拟议的模块可以集成到现有模型中,以提高细分精度.
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