通过解剖结构引导的点-声素网络进行强大而平稳的Couinaud细分
Xukun Zhang1, Sharib Ali2, Tao Liu1
1Academy for Engineering and Technology, Fudan University, Shanghai 200082, China.
Computers in biology and medicine
|September 28, 2024
概括
由于缺乏对比度,精确的肝脏细分具有挑战性. 一个新的多尺度点-声素融合框架提高了CT图像上的Couinaud细分精度,优于现有方法.
科学领域:
- 医学成像分析分析 医学成像分析
- 计算解剖学的计算解剖学
- 手术规划 手术规划
背景情况:
- 通过计算机断层扫描 (CT) 精确的Couinaud细分肝脏细分对于手术规划和病变分析至关重要.
- 由于依赖血管结构和CT扫描中邻近的肝脏段之间缺乏强度对比,细分很困难.
研究的目的:
- 从CT图像中开发一种新的框架,用于从CT图像中对肝脏细分的强大而平稳的Couinaud细分.
- 为了应对基于船只的细分和跨细分对比度限制的挑战.
主要方法:
- 设计了一个多尺度的点-声元融合框架,处理3D肝脏点云和带嵌入容器结构的声元网格.
- 该框架使用两个输入特定分支来从点和voxels中进行互补的特征提取.
- 一个局部注意模块适应性地融合了跨尺度的特征,而一个新的距离损失提高了分段之间的特征紧性.
主要成果:
- 与最先进的技术相比,拟议的方法在三个公共肝脏数据集上表现出优异的性能.
- 在LiTS数据集的分发外测试中,3DUNet在子得分上表现比UNet高20%和PointNet2Plus高8%左右.
- 实现了强大而平稳的Couinaud细分,提高了细分确定性.
结论:
- 多尺度点声元融合框架有效地建模空间关系和语义信息,以改善肝脏细分.
- 该方法在自动化Couinaud细分方面取得了重大进展,有助于手术规划和肝损伤检查.
- 代码和注释是公开的,以促进进一步的研究.
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