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Related Concept Videos

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.
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Introduction:
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Interactive content-based image retrieval with deep learning for CT abdominal organ recognition.

Chung-Ming Lo1, Chi-Cheng Wang2, Peng-Hsiang Hung2

  • 1Graduate Institute of Library, Information and Archival Studies, National Chengchi University, Taipei, Taiwan.

Physics in Medicine and Biology
|January 17, 2024
PubMed
Summary

This study developed a content-based image retrieval (CBIR) system using deep learning to automatically identify seven abdominal organs in CT scans. The system achieved high accuracy, offering valuable clinical decision support.

Keywords:
abdominal CTcontent-based image retrievaldeep learningvision transformer

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Radiology

Background:

  • Accurate identification of abdominal organs in computed tomography (CT) scans is crucial for clinical diagnosis and treatment planning.
  • Manual organ recognition can be time-consuming and requires specialized expertise.
  • Developing automated systems for organ identification can enhance efficiency and consistency in radiological assessments.

Purpose of the Study:

  • To propose and evaluate an automated content-based image retrieval (CBIR) system for recognizing seven key abdominal organs in CT slices.
  • To leverage deep learning architectures for feature extraction and organ classification.
  • To assess the system's performance in providing similar evidence for clinical use.

Main Methods:

  • A dataset of 2827 abdominal CT slices featuring liver, stomach, pancreas, spleen, right kidney, left kidney, and gallbladder was curated.
  • Deep learning models, including DenseNet, Vision Transformer (ViT), and Swin Transformer v2 (SwinViT), were fine-tuned for feature extraction.
  • The CBIR system was evaluated using classification accuracy and retrieval performance metrics.

Main Results:

  • The system achieved high classification accuracy ranging from 94% to 99% and retrieval results between 0.98 and 0.99.
  • SwinViT outperformed ViT in considering global features and multiple resolutions, while ViT surpassed DenseNet due to its superior receptive field.
  • The use of 'hole images' significantly improved performance, yielding near-perfect results across all tested deep learning architectures.

Conclusions:

  • Pretrained deep learning models, when fine-tuned with sufficient data, can effectively recognize seven abdominal organs in CT images.
  • The proposed CBIR system offers a robust method for identifying abdominal organs through similarity measurements, potentially enhancing clinical practice.
  • This automated approach provides more convincing evidence for organ recognition, opening new avenues for clinical applications.