Bui Vy, Shanbhag Sujata M, Levine Oscar, Jacobs Matthew, Bandettini W Patricia, Chang Lin-Ching, Chen Marcus Y, Hsu Li-Yueh
National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA.
Department of Electrical Engineering and Computer Science, Catholic University of America, Washington DC, USA.
IEEE Access. 2020;8:16187-16202. doi: 10.1109/access.2020.2966985. Epub 2020 Jan 15.
Contrast enhanced cardiac computed tomography angiography (CTA) is a prominent imaging modality for diagnosing cardiovascular diseases non-invasively. It assists the evaluation of the coronary artery patency and provides a comprehensive assessment of structural features of the heart and great vessels. However, physicians are often required to evaluate different cardiac structures and measure their size manually. Such task is very time-consuming and tedious due to the large number of image slices in 3D data. We present a fully automatic method based on a combined multi-atlas and corrective segmentation approach to label the heart and its associated cardiovascular structures. This method also automatically separates other surrounding intrathoracic structures from CTA images. Quantitative assessment of the proposed method is performed on 36 studies with a reference standard obtained from expert manual segmentation of various cardiac structures. Qualitative evaluation is also performed by expert readers to score 120 studies of the automatic segmentation. The quantitative results showed an overall Dice of 0.93, Hausdorff distance of 7.94 mm, and mean surface distance of 1.03 mm between automatically and manually segmented cardiac structures. The visual assessment also attained an excellent score for the automatic segmentation. The average processing time was 2.79 minutes. Our results indicate the proposed automatic framework significantly improves accuracy and computational speed in conventional multi-atlas based approach, and it provides comprehensive and reliable multi-structural segmentation of CTA images that is valuable for clinical application.
对比增强心脏计算机断层血管造影（CTA）是一种用于非侵入性诊断心血管疾病的重要成像方式。它有助于评估冠状动脉通畅情况，并对心脏和大血管的结构特征进行全面评估。然而，医生通常需要手动评估不同的心脏结构并测量其大小。由于3D数据中的图像切片数量众多，这项任务非常耗时且繁琐。我们提出了一种基于多图谱和校正分割相结合的全自动方法，用于标记心脏及其相关的心血管结构。该方法还能自动从CTA图像中分离出其他周围的胸腔内结构。我们在36项研究中对该方法进行了定量评估，参考标准是通过专家对各种心脏结构的手动分割获得的。专家读者也对120项自动分割研究进行了定性评估。定量结果显示，自动分割和手动分割的心脏结构之间的总体Dice系数为0.93，豪斯多夫距离为7.94毫米，平均表面距离为1.03毫米。视觉评估对自动分割也给出了优异的分数。平均处理时间为2.79分钟。我们的结果表明，所提出的自动框架在传统的基于多图谱的方法中显著提高了准确性和计算速度，并且它为CTA图像提供了全面且可靠的多结构分割，对临床应用具有重要价值。
