Microwave ablation (MWA) for cancer treatment is frequently monitored by ultrasound (US) B-mode imaging in the clinic, which often fails due to the low intrinsic contrast between the thermal lesion and normal tissue. Deep learning, especially convolutional neural network (CNN), has shown significant improvements in medical image analysis. Here, we propose and evaluate an US imaging based on a CNN architecture for the detection and monitoring of thermal lesions induced by MWA in porcine livers. Unlike dealing with images in many visual object recognition tasks, US radiofrequency (RF) data backscattered from the ablated region were utilized to capture features related to the thermal lesion. The dataset comprised of 1640 US RF envelope data matrices and their corresponding gross-pathology images, and were utilized for training and testing. After envelope detection, US B-mode, segmentation results based on CNN ([Formula: see text]), and modified CNN ([Formula: see text]) for US data were simultaneously reconstructed to reveal the suitability for monitoring of MWA. The [Formula: see text] and [Formula: see text] outperformed B-mode images for the detection and monitoring of MWA-induced thermal lesions. The values of the area under the receiver operating characteristic curve were 0.8728 and 0.8948 for the [Formula: see text] and [Formula: see text], respectively, which were both higher than the value of 0.6904 for B-mode images. Ablated regions that were assessed using [Formula: see text] showed a good correlation (J 0.8845, r 0.8739, and E 0.410) to gross-pathology images. This study was the first to illustrate that [Formula: see text] has the potential to detect and monitor thermal lesions, and may be utilized as an alternative modality for image-guided MWA treatments.

中文翻译：

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使用超声成像和卷积神经网络检测和监测微波消融引起的热损伤。

在临床中，经常通过超声（US）B型成像来监控用于癌症治疗的微波消融（MWA），由于热损伤和正常组织之间的固有对比度较低，因此通常会失败。深度学习，尤其是卷积神经网络（CNN），已在医学图像分析中显示出显着改进。在这里，我们提出并评估基于CNN架构的US成像，以检测和监测MWA在猪肝中诱发的热损伤。与在许多视觉对象识别任务中处理图像不同，从消融区域反向散射的US射频（RF）数据被用来捕获与热损伤有关的特征。该数据集由1640个美国RF包络数据矩阵及其相应的总体病理图像组成，并用于训练和测试。进行包络检测后，将同时重建US B模式，基于CNN（[公式：参见文本]）的分割结果和针对美国数据的经修改的CNN（[公式：参见文本]），以显示适用于监测MWA的情况。[公式：参见文字]和[公式：参见文字]在检测和监测MWA引起的热损伤方面优于B模式图像。对于[公式：参见文本]和[公式：参见文本]，接收机工作特性曲线下方的面积值分别为0.8728和0.8948，均高于B模式图像的0.6904。使用[公式：参见文字]评估的消融区域与总体病理图像显示出良好的相关性（J 0.8845，r 0.8739和E 0.410）。这项研究是第一个说明[公式：