Detection and Monitoring of Thermal Lesions Induced by Microwave Ablation Using Ultrasound Imaging and Convolutional Neural Networks

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 (SICNN), and modified CNN (SIm-CNN) for US data were simultaneously reconstructed to reveal the suitability for monitoring of MWA. The SICNN and SIm-CNN 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 SICNN and Sim-CNN, respectively, which were both higher than the value of 0.6904 for B-mode images. Ablated regions that were assessed using SIm-CNN 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 SIm-CNN has the potential to detect and monitor thermal lesions, and may be utilized as an alternative modality for image-guided MWA treatments.