Heart and great vessels segmentation in congenital heart disease via CNN and conditioned energy function postprocessing.

IF 2.3 3区 医学 Q3 ENGINEERING, BIOMEDICAL
Jiaxuan Liu, Bolun Zeng, Xiaojun Chen
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引用次数: 0

Abstract

Purpose: The segmentation of the heart and great vessels in CT images of congenital heart disease (CHD) is critical for the clinical assessment of cardiac anomalies and the diagnosis of CHD. However, the diverse types and abnormalities inherent in CHD present significant challenges to comprehensive heart segmentation.

Methods: We proposed a novel two-stage segmentation approach, integrating a Convolutional Neural Network (CNN) with a postprocessing method with conditioned energy function for pulmonary and aorta. The initial stage employs a CNN enhanced by a gated self-attention mechanism for the segmentation of five primary heart structures and two major vessels. Subsequently, the second stage utilizes a conditioned energy function specifically tailored to refine the segmentation of the pulmonary artery and aorta, ensuring vascular continuity.

Results: Our method was evaluated on a public dataset including 110 3D CT volumes, encompassing 16 CHD variants. Compared to prevailing segmentation techniques (U-Net, V-Net, Unetr, dynUnet), our approach demonstrated improvements of 1.02, 1.04, and 1.41% in Dice Coefficient (DSC), Intersection over Union (IOU), and the 95th percentile Hausdorff Distance (HD95), respectively, for heart structure segmentation. For the two great vessels, the enhancements were 1.05, 1.07, and 1.42% in these metrics.

Conclusion: The outcomes on the public dataset affirm the efficacy of our proposed segmentation method. Precise segmentation of the entire heart and great vessels can significantly aid in the diagnosis and treatment of CHD, underscoring the clinical relevance of our findings.

Abstract Image

通过 CNN 和条件能量函数后处理对先天性心脏病的心脏和大血管进行分割。
目的:先天性心脏病(CHD)CT 图像中心脏和大血管的分割对于心脏畸形的临床评估和 CHD 诊断至关重要。然而,先天性心脏病固有的不同类型和异常给全面的心脏分割带来了巨大挑战:我们提出了一种新颖的两阶段分割方法,将卷积神经网络(CNN)与肺动脉和主动脉的条件能量函数后处理方法相结合。在第一阶段,利用一个由门控自注意机制增强的 CNN 对五个主要心脏结构和两条主要血管进行分割。随后,第二阶段利用专门定制的条件能量函数来完善肺动脉和主动脉的分割,确保血管的连续性:我们的方法在一个公共数据集上进行了评估,该数据集包括 110 个三维 CT 卷,涵盖 16 种冠心病变异。与现有的分割技术(U-Net、V-Net、Unetr、dynUnet)相比,在心脏结构分割方面,我们的方法在骰子系数(DSC)、交集大于联合(IOU)和第95百分位数豪斯多夫距离(HD95)方面分别提高了1.02%、1.04%和1.41%。对于两条大血管,这些指标的提升率分别为 1.05%、1.07% 和 1.42%:结论:在公共数据集上的结果证实了我们提出的分割方法的有效性。对整个心脏和大血管的精确分割可以极大地帮助诊断和治疗先天性心脏病,这凸显了我们研究结果的临床意义。
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来源期刊
International Journal of Computer Assisted Radiology and Surgery
International Journal of Computer Assisted Radiology and Surgery ENGINEERING, BIOMEDICAL-RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
CiteScore
5.90
自引率
6.70%
发文量
243
审稿时长
6-12 weeks
期刊介绍: The International Journal for Computer Assisted Radiology and Surgery (IJCARS) is a peer-reviewed journal that provides a platform for closing the gap between medical and technical disciplines, and encourages interdisciplinary research and development activities in an international environment.
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