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Computed Tomography01:10

Computed Tomography

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Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
The technique was invented in the 1970s and is based on the principle that as X-rays pass through the body, they are absorbed or reflected at different levels. In the technique, a patient lies on a motorized platform while a computerized axial tomography (CAT) scanner rotates...
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Fully automated explainable abdominal CT contrast media phase classification using organ segmentation and machine

Yazdan Salimi1, Zahra Mansouri1, Ghasem Hajianfar1

  • 1Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, Switzerland.

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|April 17, 2024
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This study developed a machine learning method to automatically identify contrast media injection phases in CT scans. This technique enhances the usability of large medical imaging datasets for research.

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contrast‐enhanced CTdata curationdeep learningmachine learningsegmentation

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Area of Science:

  • Medical Imaging Analysis
  • Machine Learning in Radiology
  • Computational Pathology

Background:

  • Contrast-enhanced computed tomography (CECT) offers superior diagnostic information over non-enhanced CT, particularly for liver malignancies.
  • Standardization of contrast media injection phases is lacking in clinical practice and public datasets, hindering research.
  • This data variability presents a significant barrier to effectively utilizing CECT images in clinical research.

Purpose of the Study:

  • To develop and validate a machine learning-based methodology for automatically detecting contrast media injection phases from CT images.
  • To leverage organ segmentation techniques to extract relevant features for phase classification.
  • To improve the utility of CECT datasets in research by standardizing phase information.

Main Methods:

  • A dataset of 2509 CT images was classified into four phases: non-contrast, arterial, venous, and delayed.
  • Deep learning algorithms segmented seven organs and body contours.
  • First-order statistical features extracted from segmented regions were used with machine learning models (Boruta feature selection and Random Forest) for classification.

Main Results:

  • The methodology achieved high accuracy, with an average Area Under the Curve (AUC) exceeding 0.999 and an overall accuracy of 0.9936.
  • Boruta feature selection identified all relevant features, and the Random Forest model demonstrated excellent performance across all phases.
  • Misclassification rate was low (approximately 1.4%), indicating a robust and consistent performance.

Conclusions:

  • A fast, accurate, and explainable method for classifying contrast media phases in CT images was successfully developed.
  • This approach can significantly aid in data curation and annotation for large online and local CECT datasets.
  • The two-step deep learning and machine learning model facilitates more effective utilization of existing CECT imaging data for research.