College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, 310027, China.
Department of Cardiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, China.
Curr Med Sci. 2021 Apr;41(2):398-404. doi: 10.1007/s11596-021-2360-z. Epub 2021 Apr 20.
Numerous methods have been published to segment the infarct tissue in the left ventricle, most of them either need manual work, post-processing, or suffer from poor reproducibility. We proposed an automatic segmentation method for segmenting the infarct tissue in left ventricle with myocardial infarction. Cardiac images of a total of 60 diseased hearts (55 human hearts and 5 porcine hearts) were used in this study. The epicardial and endocardial boundaries of the ventricles in every 2D slice of the cardiac magnetic resonance with late gadolinium enhancement images were manually segmented. The subsequent pipeline of infarct tissue segmentation is fully automatic. The segmentation results with the automatic algorithm proposed in this paper were compared to the consensus ground truth. The median of Dice overlap between our automatic method and the consensus ground truth is 0.79. We also compared the automatic method with the consensus ground truth using different image sources from different centers with different scan parameters and different scan machines. The results showed that the Dice overlap with the public dataset was 0.83, and the overall Dice overlap was 0.79. The results show that our method is robust with respect to different MRI image sources, which were scanned by different centers with different image collection parameters. The segmentation accuracy we obtained is comparable to or better than that of the conventional semi-automatic methods. Our segmentation method may be useful for processing large amount of dataset in clinic.
已经有许多方法被发表用于分割左心室的梗死组织，其中大多数方法要么需要手动操作、后处理，要么存在可重复性差的问题。我们提出了一种用于分割心肌梗死后左心室梗死组织的自动分割方法。本研究共使用了 60 个患病心脏（55 个人类心脏和 5 个猪心脏）的心脏图像。手动分割心脏磁共振晚期钆增强图像中每 2D 切片的心室心外膜和心内膜边界。随后的梗死组织分割流程是全自动的。将本文提出的自动算法的分割结果与共识的金标准进行比较。我们的自动方法与共识金标准之间的 Dice 重叠中位数为 0.79。我们还使用来自不同中心的不同图像源、不同扫描参数和不同扫描机比较了自动方法与共识金标准。结果表明，与公共数据集的 Dice 重叠为 0.83，总体 Dice 重叠为 0.79。结果表明，我们的方法对于不同的 MRI 图像源具有鲁棒性，这些图像源是由不同中心使用不同的图像采集参数扫描的。我们获得的分割准确性可与传统的半自动方法相媲美或更好。我们的分割方法可能有助于在临床上处理大量数据集。
