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.  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 SICNN and SIm-CNN outperformed B-mode images for the detection and monitoring of MWA-induced thermal lesions.  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.
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