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PlaqueNet:深度学习使冠状动脉斑块细分从冠状动脉计算机断层扫描血管造影

Linyuan Wang¹, Xiaofeng Zhang², Congyu Tian³

¹Department of Cardiovascular Surgery, the Affiliated Hospital of Shanxi Medical University, Shanxi Cardiovascular Hospital (Institute), Shanxi Clinical Medical Research Center for Cardiovascular Disease, Taiyuan, 030024, Shanxi, China.

Visual computing for industry, biomedicine, and art

|March 22, 2024

概括

PlaqueNet使用先进的深度学习精确地从CCTA图像中对冠状动脉斑块进行细分. 这种早期发现动脉样硬化斑块有助于降低心血管疾病的风险.

科学领域:

医疗成像医学成像
人工智能的人工智能
心脏病学心脏病学

背景情况:

由动脉样硬化斑块驱动的心血管疾病对健康构成重大风险.
早期发现冠状动脉斑块对于有效治疗和预防疾病至关重要.
冠状动脉计算机断层扫描血管造影 (CCTA) 是可视化这些斑块的关键成像方式.

研究的目的:

介绍PlaqueNet,一个新的深度学习模型,用于在CCTA图像中对冠状动脉斑块进行细分.
通过使用先进的神经网络模块来提高特征提取和细分的准确性.
为了提高斑块细分算法的整体效率.

主要方法:

利用先进的残余网络与深度残余优化模块来进行强大的特征提取.
引入了一个深度的形空间金字塔与双立方高效通道注意力 (DASPP-BICECA) 模块的聚合,以提高细分精度.
实施了BINet,一个联合网络损失评估模块,以优化细分模型而不会影响结果.

主要成果:

与CCTA细分中的其他三种算法相比,PlaqueNet取得了更高的性能.
关键绩效指标包括交叉在欧盟 (IoU) 的87.37%和子系数的93.26%.
较高的平均IoU (93.68%) 和平均Dice (96.63%) 评分显示出强大的细分能力.

结论:

PlaqueNet有效地从CCTA图像中对冠状动脉斑块进行细分,优于现有的方法.
整合DASPP-BICECA和BINet模块显著提高了细分精度和模型效率.
这种方法有望改善心血管疾病的早期检测和管理.

关键词:

注意力机制注意力机制冠状动脉上的斑块深度残留网络深度残留网络医疗图像细分医疗图像细分

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