# AutoCT: Automated CT registration, segmentation, and quantification

arXiv: https://arxiv.org/abs/2310.17780 · HTML: https://mlsanity.com/paper/2310.17780/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 22 references that are in the map.
- First posted on arXiv 26 Oct 2023.

## Builds on (most cited first, 3 of 3)

- [A Survey on Deep Learning in Medical Image Analysis](https://mlsanity.com/paper/1702.05747/) (2017)
- [The Medical Segmentation Decathlon](https://mlsanity.com/paper/2106.05735/) (2021)
- [Deep Learning in Medical Image Registration: A Review](https://mlsanity.com/paper/1912.12318/) (2019)

## Built on by (most cited first, 1 of 1)

- [CT-PrepAgent: Bounded Policy and Controlled Execution for Adaptive CT Data Preparation](https://mlsanity.com/paper/2608.01233/) (2026)

## Abstract (arXiv)

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.