Measurement of thermally ablated lesions in sonoelastographic images using level set methods

Abstract

The capability of sonoelastography to detect lesions based on elasticity contrast can be applied to monitor the creation of thermally ablated lesion. Currently, segmentation of lesions depicted in sonoelastographic images is performed manually which can be a time consuming process and prone to significant intra-and inter-observer variability. This work presents a semi-automated segmentation algorithm for sonoelastographic data. The user starts by planting a seed in the perceived center of the lesion. Fast marching methods use this information to create an initial estimate of the lesion. Subsequently, level set methods refine its final shape by attaching the segmented contour to edges in the image while maintaining smoothness. The algorithm is applied to in vivo sonoelastographic images from twenty five thermal ablated lesions created in porcine livers. The estimated area is compared to results from manual segmentation and gross pathology images. Results show that the algorithm outperforms manual segmentation in accuracy, inter-and intra-observer variability. The processing time per image is significantly reduced.

Performance of three-dimensional sonoelastography in prostate cancer detection: A comparison between ex vivo and in vivo experiments

“…The highest frequency which allowed an image of good quality was selected for further analysis. Deficits in sonoelastographic volumes were identified by achieving a consensus of 3 observers and segmented using the same techniques described in [18] but extended to 3D domain. Discrete dynamic contours were used to outline the boundary of the prostate gland in each of the US B-mode images resulting in a 3D representation of the surface of the gland [19].…”

Clinical applications of sonoelastography

“…The measurements performed with sonoelastography had over 90% correlation with the size of the lesions in the gross pathology images (ground truth). By introducing an automatic algorithm, these measurements could be performed in real-time [7,8] while keeping results similar to manual segmentation. Recently, a new imaging technique based on sonoelastography has emerged: Crawling wave (CrW) sonoelastography.…”