论文标题
使用深度学习的2D超声心动图中左心室的自动分割
Automated Segmentation of Left Ventricle in 2D echocardiography using deep learning
论文作者
论文摘要
在成功地应用了U-NET到医学图像之后，提出了不同的编码器模型，以改进原始U-NET，用于分割超声心动图图像。这项研究旨在通过将其应用于2D自动将左心室的内膜段分割，以检查最先进的拟议模型以及原始的U-NET模型的性能。模型的预测输出用于通过将自动结果与专家注释（黄金标准）进行比较来评估模型的性能。我们的结果表明，U-NET模型通过达到0.92 $ \ pm 0.05 $的平均骰子系数和Hausdorff的距离为3.97 $ \ pm 0.82 $，从而优于其他模型。
Following the successful application of the U-Net to medical images, there have been different encoder-decoder models proposed as an improvement to the original U-Net for segmenting echocardiographic images. This study aims to examine the performance of the state-of-the-art proposed models as well as the original U-Net model by applying them to segment the endocardium of the Left Ventricle in 2D automatically. The prediction outputs of the models are used to evaluate the performance of the models by comparing the automated results against the expert annotations (gold standard). Our results reveal that the U-Net model outperforms other models by achieving an average Dice coefficient of 0.92$ \pm 0.05$, and Hausdorff distance of 3.97$ \pm 0.82$.
