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Related Concept Videos

Ultrasonography01:17

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Endoscopic Ultrasound (EUS) and FibroScan are valuable diagnostic tools in gastroenterology and hepatology, each with specific applications and techniques.
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Ultrasound I: Abdominal Ultrasonography01:20

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Introduction:
Abdominal ultrasonography, commonly known as abdominal ultrasound, is a vital, non-invasive medical imaging technique widely used in healthcare.
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Robust Automatic Grading of Blunt Liver Trauma in Contrast-Enhanced Ultrasound Using Label-Noise-Resistant Models.

Tianci Zhang1,2, Rui Li1,2, Zhaoming Zhong3

  • 1School of Biological Science and Medical Engineering, Southeast University, Nanjing, 211189, China.

Journal of Imaging Informatics in Medicine
|March 10, 2025
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Summary

This study introduces a novel AI model to accurately diagnose liver trauma from contrast-enhanced ultrasound (CEUS) images. The Label-Noisy-Resistant CNN-Transformer Hybrid Architecture (LNRHA) effectively handles image noise and diagnostic variations for improved classification.

Keywords:
CNN-Transformer hybrid architectureContrast-Enhanced ultrasound imageLiver Trauma classificationNoisy label filter

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Trauma Surgery

Background:

  • Contrast-enhanced ultrasound (CEUS) shows promise for diagnosing liver trauma, a leading cause of blunt abdominal trauma mortality.
  • CEUS image analysis for liver trauma is challenging due to speckle noise and complex visual features, relying heavily on radiologist expertise.
  • Intra- and inter-observer variability significantly impacts the accuracy of CEUS-based liver trauma diagnosis.

Purpose of the Study:

  • To develop an automated system for accurate liver trauma classification using CEUS images.
  • To address the challenges of image noise and diagnostic variability in CEUS liver trauma assessment.
  • To improve the reliability and efficiency of liver trauma diagnosis through advanced AI techniques.

Main Methods:

  • Proposed a CNN-Transformer-based Self-Contextual Dual Transformer (SCDT) module for feature extraction from CEUS images.
  • Introduced a Confidence-Based Label Filter (CLF) module to identify and mitigate annotation noise caused by observer variability.
  • Developed a novel loss function to penalize uncertain data, enabling the model to utilize all data while preventing overfitting.

Main Results:

  • The proposed Label-Noisy-Resistant CNN-Transformer Hybrid Architecture (LNRHA) achieved promising performance on an in-house liver trauma CEUS dataset.
  • LNRHA demonstrated superior performance compared to state-of-the-art methods, particularly on datasets with label noise.
  • The model effectively handles speckle noise and diagnostic variations inherent in CEUS liver trauma imaging.

Conclusions:

  • The LNRHA offers an effective and robust solution for automated liver trauma classification from CEUS images.
  • The developed approach significantly improves diagnostic accuracy by addressing label noise and observer variability.
  • This AI-driven method has the potential to enhance the clinical utility of CEUS in managing blunt abdominal trauma.