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AutoCT: Automated CT registration, segmentation, and quantification

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arxiv 2310.17780 v1 pith:UPUDCXCK submitted 2023-10-26 eess.IV cs.CV

classification eess.IVcs.CV
keywords autoctsegmentationanalysisapplicationsimagingpipelinequantificationregistration
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. CT-PrepAgent: Bounded Policy and Controlled Execution for Adaptive CT Data Preparation

    cs.AI 2026-08 conditional novelty 6.0 of 10

    An LLM agent constrained to a fixed CT preprocessing menu, with deterministic execution and verification, raised verified output yield and matched baseline utility on three public and two private cohorts.

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