AutoCT: Automated CT registration, segmentation, and quantification
This provides a new toolkit for the CT imaging community to support AI-driven applications, but it appears incremental as it integrates existing methods into a comprehensive pipeline.
The authors tackled the problem of automating CT image analysis by developing AutoCT, an end-to-end pipeline for preprocessing, registration, segmentation, and quantification of 3D CT scans, which enables atlas-based segmentation and downstream statistical learning for medical diagnostics.
The processing and analysis of computed tomography (CT) imaging is important for both basic scientific development and clinical applications. In AutoCT, we provide a comprehensive pipeline that integrates an end-to-end automatic preprocessing, registration, segmentation, and quantitative analysis of 3D CT scans. The engineered pipeline enables atlas-based CT segmentation and quantification leveraging diffeomorphic transformations through efficient forward and inverse mappings. The extracted localized features from the deformation field allow for downstream statistical learning that may facilitate medical diagnostics. On a lightweight and portable software platform, AutoCT provides a new toolkit for the CT imaging community to underpin the deployment of artificial intelligence-driven applications.