Quickly and accurately segmenting the left ventricular (LV) myocardium from ultrasound images and measuring the thickness of the interventricular septum and LV wall play an important role in hypertrophic cardiomyopathy. However, the segmentation of the LV myocardium is a challenging task due to image blurring and individual differences. We attempted to perform LV segmentation in ultrasound images using the encoder–decoder architecture of U-Net and other networks and found it to be not accurate enough. Therefore, we propose a novel multi-task cascaded convolutional neural network (called MTC-Net) to segment the LV myocardium from echocardiography. MTC-Net contains two parts: One is pre-trained Resnet-34 followed by two decoder branches for mask and boundary detection, and the other module is pre-trained with many improved novel encoder–decoder architectures for extracting more detailed features. Both parts of the network use the atrous spatial pyramid pooling module to capture high-level text information. A hybrid loss function is engaged for mask and contour prediction. The network is trained and evaluated with echocardiographic images, which are labeled manually by doctors. The comparison study with other networks shows that MTC-Net has better accuracy and performance. MTC-Net achieves state-of-the-art performance on the test set. The mean value of the dice coefficient is 0.9442 and the mean value of intersection over union is 0.8951.

中文翻译：

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基于超声心动图的级联卷积神经网络自动分割左心室心肌

快速，准确地从超声图像中分割左心室（LV）心肌，并测量室间隔和LV壁的厚度在肥厚型心肌病中起重要作用。但是，由于图像模糊和个体差异，LV心肌的分割是一项艰巨的任务。我们尝试使用U-Net和其他网络的编码器-解码器体系结构在超声图像中执行LV分割，发现它不够准确。因此，我们提出了一种新颖的多任务级联卷积神经网络（称为MTC-Net），用于从超声心动图上分割左心室心肌。MTC-Net包含两个部分：一个是预训练的Resnet-34，其后是两个用于掩码和边界检测的解码器分支，其他模块则经过了许多改进的新颖编码器-解码器体系结构的预训练，以提取更多详细功能。网络的两个部分都使用无空间金字塔池化模块来捕获高级文本信息。混合损失函数用于掩模和轮廓预测。使用超声心动图图像对网络进行训练和评估，这些图像由医生手动标记。与其他网络的比较研究表明，MTC-Net具有更好的准确性和性能。MTC-Net在测试仪上实现了最先进的性能。骰子系数的平均值为0.9442，联合交集的平均值为0.8951。混合损失函数用于掩模和轮廓预测。使用超声心动图图像对网络进行训练和评估，这些图像由医生手动标记。与其他网络的比较研究表明，MTC-Net具有更好的准确性和性能。MTC-Net在测试仪上实现了最先进的性能。骰子系数的平均值为0.9442，联合交集的平均值为0.8951。混合损失函数用于掩模和轮廓预测。使用超声心动图图像对网络进行训练和评估，这些图像由医生手动标记。与其他网络的比较研究表明，MTC-Net具有更好的准确性和性能。MTC-Net在测试仪上实现了最先进的性能。骰子系数的平均值为0.9442，联合交集的平均值为0.8951。