CIS-UNet:在计算机断层扫描中通过情境意识的转移窗口自我注意力对大动脉进行多类细分,通过计算机断层扫描进行血管学
Muhammad Imran1, Jonathan R Krebs2, Veera Rajasekhar Reddy Gopu3
1Department of Medicine, University of Florida, Gainesville, FL 32611, USA.
概括
一个新的深度学习模型,即上下文注入的Swin-UNet (CIS-UNet),准确地对大动脉及其分支进行细分,用于最少的侵入性治疗. 这种先进的3D细分可以改善大动脉疾病干预中的手术规划和内移植结构.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 心血管外科心血管外科
背景情况:
- 对大动脉疾病的最小侵入性治疗依赖于大动脉及其分支的精确3D细分.
- 以前的细分方法往往过于简化了这个问题,忽视了个体大动脉分支的分化,这对于手术规划至关重要.
- 不准确的细分可以导致手术规划和内移植结构的错误,可能会损害患者的治疗结果.
研究的目的:
- 引入语境注入的Swin-UNet (CIS-UNet),这是一个新的深度学习模型,用于对大动脉及其13个主要分支进行多类3D细分.
- 提高大动脉细分的准确性和效率,以改善内血管治疗的规划.
- 提供公开可用的数据集和代码,用于大动脉细分的可重复性研究.
主要方法:
- 开发CIS-UNet,这是一个结合卷积神经网络 (CNN) 和Swin变压器的深度学习模型.
- 在瓶处实现一个层次化的编码器-解码器结构,并采用一种新的上下文感知转移窗口自我注意 (CSW-SA) 模块.
- 使用四倍交叉验证对59名患者的计算机断层扫描 (CT) 进行CIS-UNet的评估.
主要成果:
- CIS-UNet实现了0.732的优越平均子系数,超过了最先进的Swin UNetR (0.717).
- 该模型显示,较低的平均表面距离为2.40毫米,而Swin UNetR的2.75毫米为2.75毫米.
- CIS-UNet显示了计算效率和细分精度的提高,特别是由于CSW-SA模块对补丁合并层的调整.
结论:
- CIS-UNet在3D大动脉和分支细分精度方面取得了重大进展.
- 该模型的增强细分功能为规划内血管治疗提供了更好的优化.
- 数据集和代码的可用性促进了该领域的进一步研究和开发.
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