Radiofrequency ablation (RFA) is a minimally invasive thermal therapy that is under investigation as an alternative to surgery for treating liver tumors. Currently, there is a need to monitor the process of lesion creation to guarantee complete treatment of the diseased tissue. In a previous study, sonoelastography was used to detect and measure RFA lesions during exposed liver experiments in a porcine model in vivo. Manual outlining of these lesions in the sonoelastographic images is challenging due to a lack of boundary definition and artifacts formed by respiratory motion and perfusion. As a result, measuring the lesions becomes a time-consuming process with high variability. This work introduces a semi-automatic segmentation algorithm for sonoelastographic data based on level set methods. This algorithm aims to reduce the variability and processing time involved in manual segmentation while maintaining comparable results. For this purpose, eleven RFA lesions are created in five porcine livers exposed through a midline incision. Three independent observers perform manual and semi-automatic measurements on the in vivo sonoelastographic images. These results are compared to measurements from gross pathology. In addition, we assess the feasibility of performing sonoelastograhic measurements transcutaneously. The procedure previously described is repeated with three more lesions without exposing the liver. Overall, the semi-automatic algorithm outperforms manual segmentation in accuracy, speed, and repeatability. These results suggest that sonoelastography in combination with the segmentation algorithm has the potential to be used as a complementary technique to conventional ultrasound for thermal ablation monitoring and follow-up imaging.