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深度解剖学学习用于肺气道和动脉静脉建模,使用对比增强的CT合成

Hanxiao Zhang¹, Minghui Zhang¹, Yun Gu^2,3

¹Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, China.

International journal of computer assisted radiology and surgery

|May 31, 2023

概括

这项研究引入了一种新的深度学习模型,用于同时从非对比CT扫描中对肺气道和动脉静脉结构进行细分,并生成合成对比增强CT图像. 综合方法提高了解剖细分的准确性和图像现实性,以更好地进行内支骨干预.

科学领域:

医疗成像医学成像
人工智能的人工智能
肺部医学肺部医学

背景情况:

对肺部解剖结构的准确建模,如气道和动脉静脉地图,对于内分支干预至关重要.
当前的方法通常使用自动细分方法独立于非对比计算断层扫描 (NCCT) 来细分这些结构.
需要综合方法,可以同时对多个解剖结构进行细分,并提高图像质量.

研究的目的:

开发和评估一种基于卷积神经网络 (CNN) 的新型模型,用于肺气道和动脉静脉图的联合细分.
为了从NCCT图像中实现合成对比增强CT (CECT) 生成.
探索多任务学习的潜力,以整合细分和图像合成任务.

主要方法:

一个多任务学习框架被设计为同时生成三个细分图和合成 CECTs.
一种具有组织知识相互作用的协作学习模型被用于肺气道和动脉静脉细分.
一个有条件的对抗性训练策略,通过CECT身份和重建规范化,被应用到NCCT到CECT翻译,以动脉图为指导.

主要成果:

与现有方法相比,拟议的框架在三个数据集中表现出优异的性能.
在气道细分 (93.6%),动脉-静脉细分 (80.7%) 和动脉细分 (82.4%) 中获得了高的子得分系数.
成功生成现实的CECT与准确的细分地图同时生成,经过废除研究验证.

结论:

开发的模型显示了多任务学习在整合解剖学相关细分和NCCT到CECT翻译方面具有重大潜力.
互动方法相互有利于细分的准确性和合成的可信性.
这种综合方法为肺部干预的术前规划和分析提供了有希望的进步.

关键词:

计算机辅助干预是指计算机辅助干预.图像细分图像细分图像细分图像合成图像合成多任务学习是多任务学习.

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