基于CNN的全自动中枢抽取,使用CT图像和存在概率图
Yukiteru Masuda1, Ryo Ishikawa1, Toru Tanaka1
1Canon Inc., 30-2, Shimomaruko 3-chome, Ohta-ku, Tokyo 146-8501, Japan.
Physics in medicine and biology
|December 15, 2023
概括
这项研究引入了一种自动化方法,可以从CT图像中提取心肌形状,从而提高心脏手术的准确性. 这种新的方法提高了 mitra 膜细分的精度,以便更好地规划治疗.
科学领域:
- 医疗成像医学成像
- 心血管外科心血管外科
- 人工智能在医学中的应用
背景情况:
- 从断层扫描图像中精确地提取心肌形状,对于计划手术和干预治疗至关重要.
- 手动提取是耗时的,现有的自动化方法缺乏足够的准确性.
- 由于心脏循环和患者解剖学的变化,心交膜细分具有挑战性.
研究的目的:
- 开发一种完全自动化的方法,从所有心脏阶段的计算机断层扫描 (CT) 图像中提取心肌形状.
- 与现有方法相比,提高中枢形状提取的准确性.
- 提供一个工具,以加强对关节干预的规划.
主要方法:
- 开发了一种利用DenseNet和U-Net的新方法来提取心肌形状.
- 该方法结合了原始CT图像和U-Net推断的中枢膜区域的概率图.
- 该方法在204名患者的1585张CT图像上进行了训练和验证,使用了10倍交叉验证.
主要成果:
- 拟议的自动化方法实现了0.88mm的平均形状提取误差.
- 与没有概率图的方法相比,这代表了0.32毫米的显著改进.
- 存在概率图的使用提高了中枢细分的准确性.
结论:
- 一种新的,完全自动化的方法用于从4DCT图像中提取心肌形状.
- 存在概率图的整合显著提高了中枢形状提取的准确性.
- 这一进步有助于在心脏干预中进行更精确的手术前规划.
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