Active Contour for Applying CT Segmentation on a Single Image to Surrounding Multiple Images

There is a growing interest in using X-ray CT scanners to measure industrial products, as CT scan data holds significant potential for reverse engineering and model-based design. To fully utilize CT scan data from assembled parts, it is essential to segment each individual component. The commonly used thresholding method is ineffective when there is no luminance difference at the boundaries between parts. Consequently, segmentation becomes particularly challenging when adjacent parts are made of the same material (i.e., have the same density). When threshold-based segmentation fails, manual interventions such as outlining objects with mouse clicks is often required. However, manual segmentation on a single image may lead to misalignments in surrounding slices, even though contour shapes generally remain similar within a certain region. If artificial intelligence can correct these misalignments and propagate contours across multiple slices, it could enable segmentation in many cases that are currently unfeasible with existing technologies. In this study, we propose a deep learning-based active contour algorithm for applying the manual segmentation performed on a single image to its surrounding slices.