DRU-Net:通过密集的残留U-Network与混合损失功能的肺动脉细分
Manahil Zulfiqar1,2, Maciej Stanuch1,2, Marek Wodzinski1,2
1Department of Measurement and Electronics, AGH University of Science and Technology, 30-059 Krakow, Poland.
Sensors (Basel, Switzerland)
|July 8, 2023
概括
肺动脉的准确细分对于胸部手术规划至关重要. 一个新的深度神经网络,密集的残留U-Net,有效地细分这些复杂的血管,改善手术准备.
科学领域:
- 医疗成像医学成像
- 计算机辅助诊断 计算机辅助诊断
- 胸部外科手术 胸部外科手术
背景情况:
- 肺动脉结构对于胸部医学治疗至关重要.
- 肺血管的复杂解剖学使区分动脉和静脉变得复杂.
- 肺动脉的自动细分是具有挑战性的,因为不规则的形状和相邻的组织.
研究的目的:
- 开发一个准确的深度神经网络,用于细分肺动脉的拓结构.
- 为了提高性能并防止肺动脉细分模型中的过拟合.
主要方法:
- 为肺动脉细分提出了一个密集的残留U-Net架构.
- 该网络使用增强型计算机断层扫描 (CT) 卷进行了训练.
- 实施了混合损失函数以提高细分精度.
主要成果:
- 与最先进的方法相比,拟议的密度残留U-Net实现了较好的Dice和HD95分数.
- 获得了0.8775的平均子得分和4.2624毫米的HD95得分.
- 使用增强CT数据和混合损失功能的网络训练提高了性能,并防止了过度装配.
结论:
- 发达的深度神经网络为肺动脉细分提供了强大的解决方案.
- 这种方法有助于医生在胸部手术的术前规划.
- 对肺动脉进行准确的评估对于成功的手术结果至关重要.
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