Semiautomated editing of computed tomography sections for visualization of vasculature

The goal of our work is to help radiologists remove obscuring structures from a large volume of computed tomography angiography (CTA) images by editing a small number of sections prior to three-dimensional (3D) reconstruction. We combine automated segmentation of the entire volume with manual editing of a small number of sections. The segmentation process uses a neural network to learn thresholds for multilevel thresholding and a constraint- satisfaction neural network to smooth the boundaries of labeled segments. Following segmentation, the user edits a small number of images by pointing and clicking, and then a connectivity procedure automatically selects corresponding segments from other sections by comparing adjacent voxels within and across sections for label identity. Our results suggest that automated segmentation followed by minimal manual editing is a promising approach to editing of CTA sequences. However, prerequisites to clinical utility are evaluation of segmentation accuracy and development of methods for resolution of label ambiguity.