SAACT: Semiautomated Annotation of Computerized Tomography Data

A successful deep learning-based solution design requires a large volume of well-annotated data to ensure model generalizability and efficient deployment. For certain advanced applications, such as semantic segmentation, the training dataset must be manually annotated by assigning labels to each pixel in the images. This labor-intensive and time-consuming process must be performed and verified by domain experts. This article presents a semiautomated data annotation technique for X-ray computed tomography (XCT) data, leveraging computer-aided design (CAD) design files. The proposed system employs various preprocessing techniques, including noise filtering and background removal. Additionally, we introduce an improved 3-D volume registration method based on the diffusion imaging in python (DIPY) library. The proposed annotation framework was applied to both real and semantic XCT datasets for an industrial tool and validated using a semantic segmentation model. The trained model achieved intersection over union (IoU) scores of 0.70 and 0.64 for the real and semantic XCT data, respectively. These results demonstrate the effectiveness of the annotation method, indicating strong performance in both cases. The findings confirm that the framework can be integrated into artificial intelligence (AI)-based industrial inspection systems to accelerate the industrial inspection processes, improve defect detection accuracy, and enable automated report generation.