Applying a Single Segmentation on X-Ray CT Volume to Surrounding Multiple Images

There is growing interest in using X-ray computed tomography (CT) for inspecting industrial products, owing to its potential for reverse engineering and model-based design. However, to fully utilize CT scan data of assembled components, individual part segmentation is essential. Conventional thresholding methods are often ineffective when there is no luminance contrast at the boundaries between parts, making it particularly challenging to segment adjacent parts com-posed of the same material. Manual segmentation—such as outlining a target object on a single slice—can lead to contour misalignment across neighboring slices, despite the generally similar contour shapes within localized regions. To address this, we propose a deep learning-based active contour algorithm that propagates a manually segmented contour from a single slice to adjacent slices, correcting positional errors and aligning contours across cross sections. Experimental results demonstrate that our method reduces the need for manual segmentation to approximately one slice for every 20.