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.
@article{arxiv.2310.17780,
title = {AutoCT: Automated CT registration, segmentation, and quantification},
author = {Zhe Bai and Abdelilah Essiari and Talita Perciano and Kristofer E. Bouchard},
journal= {arXiv preprint arXiv:2310.17780},
year = {2023}
}