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D2 - RD-UNet:一个双阶段的双类框架,用于肝血管细分的连接性纠正

Matteo Cavicchioli¹, Andrea Moglia², Guillaume Garret³

¹Department of Electronics, Information, and Bioengineering, Politecnico di Milano, Via Giuseppe Ponzio 34, 20133 Milan, Lombardy, Italy; Fondazione MIAS (AIMS Academy), Ospedale Niguarda, Piazza dell'Ospedale Maggiore 3, 20162 Milan, Lombardy, Italy.

Computers in biology and medicine

|June 28, 2025

概括

我们开发了D2-RD-UNet,这是一种新的深度学习框架,用于肝脏成像中的肝脏和门脉的精确细分. 这种方法通过提高准确性和保持血管连续性来改善肝脏手术的术前规划.

科学领域:

医疗成像医学成像
深度学习 (Deep Learning) 是一种深度学习.
计算解剖学的计算解剖学

背景情况:

肝脏和门静脉的准确细分对于肝脏手术规划至关重要.
现有的深度学习模型与解剖学变异性,病理变化和阶级不平衡作斗争,限制了临床适用性.
挑战包括对患者进行概括,并保持血管拓.

研究的目的:

引入D2-RD-UNet,一个双阶段,双类细分框架,用于肝脏和门血管.
为了提高肝血管细分的准确性和稳定性,用于外科手术规划.
为了解决当前深度学习方法在处理复杂的肝血管系统方面的局限性.

主要方法:

拟议的D2-RD-UNet架构具有密集和残余连接,以增强功能传播.
集成的先进数据驱动预处理和3D/4DCT数据的双路径架构,包括容器波器.
开发了新的后处理算法:多类船舶连接性校正和基于半径的分支评估.
策划并利用了AIMS-HPV-385数据集,这是一个大规模的,经过验证的数据集,包含385个病理CT扫描.

主要成果:

在独立的测试组 (AIMS-HPV-385和3D-IRCADb-01) 上,D2-RD-UNet变体表现出优于最先进的方法的性能.
在各种患者数据中实现了强大的概括,有效地保持了血管连续性.
该模型提供了可靠的肝血管重建,性能优于现有技术.

结论:

D2-RD-UNet框架为肝脏和门静脉细分提供了可靠和准确的解决方案.
这种方法显著提高了肝切除和移植的术前规划能力.
开发的数据集和算法有助于在肝胆外科手术中推进医学图像分析.

关键词:

肝血管细分肝血管细分基于局部半径的血管指标.肝血管的多类细分肝血管的多类细分船舶连接性纠正船舶连接性纠正

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