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Segment-and-Classify: ROI-Guided Generalizable Contrast Phase Classification in CT Using XGBoost.

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This study automates contrast phase classification in CT scans using organ-specific features and a lightweight decision tree model. The method shows strong performance and generalizability across diverse datasets.

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Area of Science:

  • Medical Imaging
  • Radiology
  • Artificial Intelligence in Medicine

Background:

  • Accurate contrast phase classification in CT scans is crucial for diagnosis.
  • Manual classification is time-consuming and prone to inter-observer variability.
  • Automated methods can improve efficiency and consistency in radiological workflows.

Purpose of the Study:

  • To develop and validate an automated system for contrast phase classification in CT images.
  • To utilize organ-specific features extracted by TotalSegmentator for classification.
  • To employ a lightweight decision tree classifier for efficient computation.

Main Methods:

  • Retrospective analysis of three public CT datasets (WAW-TACE, VinDr-Multiphase, C4KC-KiTS).
  • Feature extraction using TotalSegmentator, a widely used segmentation tool.
  • Classification using a gradient-boosted decision tree model.

Main Results:

  • The model achieved high AUCs (>0.937 on VinDr-Multiphase, >0.991 on C4KC-KiTS) across all phases.
  • Superior F1-scores were observed in non-contrast, arterial, and delayed phases.
  • The model demonstrated robust generalizability across datasets from different institutions.

Conclusions:

  • A lightweight, automated model effectively classifies CT contrast phases.
  • The approach shows strong performance and generalizability, outperforming baseline models.
  • This method offers a promising solution for automating contrast phase classification in clinical practice.