Semiautomated thermal lesion segmentation for three-dimensional elastographic imaging

Several studies have demonstrated that lesion volumes computed from multiple planar slices through the region-of-interest (ROI) are more accurate than volumes estimated assuming simple shapes and incorporating single or orthogonal diameter estimates. However, manual delineation of boundaries on multiple planar 2-D images is tedious and labor-intensive. Automatic extraction of lesion boundaries is, therefore, attractive and imperative to remove subjectivity and reduce assessment time. This paper presents a semiautomated segmentation algorithm for thermal lesions on 3-D elastographic data to obtain both area and volume information. The semiautomated segmentation algorithm is based on thresholding and morphologic opening of both 2-D and 3-D elastographic data. Results obtained on 44 thermal lesions imaged in vitro using elastography were compared to manual delineation of both elastographic and pathology images. Results obtained using semiautomated segmentation demonstrate a close correspondence with manual delineation results. However, area and volume estimates obtained using both manual and semiautomated segmentation of lesions seen on elastograms slightly underestimate areas and volumes measured from pathology.