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开发和评估一种新的器官特异生物力学引导的对比增强CT体积合成模型
Shilun Du1, Mengruo Shen1, Yibiao Liu1
1State Key Laboratory of Fluid Power & Mechatronic Systems, Zhejiang University, Hangzhou, Zhejiang, People's Republic of China.
Physics in medicine and biology
|October 15, 2025
概括
本研究引入了一种新的生物力学导向模型,用于生成具有较大变形的多样化的对比增强计算机断层扫描 (CECT) 卷,通过数据集增强显著改善下游细分和注册任务.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 生物医学工程 生物医学工程
背景情况:
- 与对比度增强的计算机断层扫描 (CECT) 是至关重要的,但数据集的获取和注释是耗时的.
- 现有的生成模型与多样化的CECT数据扎,特别是具有很大的变形.
- 增加CECT数据集对于改善下游医疗图像分析任务至关重要.
研究的目的:
- 开发一种新的生物力学引导的CECT体积合成模型,用于生成变形的CECT体积.
- 评估变形增强CECT数据集对下游任务 (如细分和注册) 的有效性.
- 使用生成模型增强CECT数据集的多样性和规模.
主要方法:
- 使用条件生成对抗网络 (cGANs) 开发了一个生物力学引导的可变形CECT体积合成框架.
- 采用顺序变形来产生时间一致的CECT体积,并纳入过渡区域生成和对比度调整的模块.
- 在肝脏和脏CECT数据集上训练模型进行增强,并通过定性和定量测试验证合成体积的真实性.
主要成果:
- 合成的CECT卷显示出高保真度,平均子相似系数 (DSC) 为0.838,结构相似度指数 (SSIM) 为0.988.
- 分段任务显示显著改善,增强数据集的平均mAP@50得分为0.641,而没有增强的数据集是0.399.
- 通过增加增强训练,可变形图像记录性能提高了7%,优于现有方法.
结论:
- 拟议的模型有效地合成可变形的CECT卷,增强数据集的多样性和大小.
- 合成的CECT卷显示出良好的连续性和与真实CECT数据的感知相似性.
- 增强的CECT数据集显著提高了下游任务的性能,例如细分和可变形图像注册.
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