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3D Universal Lesion Detection and Tagging in CT with Self-Training.

Jared Frazier1, Tejas Sudharshan Mathai1, Jianfei Liu1

  • 1Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda MD, USA.

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Summary

This study introduces a self-training pipeline for 3D lesion detection and tagging in CT scans. The method achieves high sensitivity using a limited dataset, aiding radiologists in lesion measurement and tumor burden assessment.

Keywords:
3D ContextCTClassificationDetectionLesionNeural NetworksSelf-Training

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Radiology

Background:

  • Radiologists face challenges in lesion localization, classification, and measurement in CT studies.
  • Universal lesion detection and tagging (ULDT) aims to streamline these tasks and enable tumor burden assessment.
  • Existing ULDT methods struggle with incomplete volumetric data and class imbalance in datasets like DeepLesion.

Purpose of the Study:

  • To develop a self-training pipeline for detecting and tagging 3D lesions based on their body part location.
  • To overcome limitations of existing datasets and methods in 3D lesion analysis.

Main Methods:

  • A VFNet model was trained on a 30% subset of the DeepLesion dataset for 2D lesion detection and tagging.
  • A self-training approach was employed, expanding 2D lesion context to 3D and iteratively retraining the model.
  • The pipeline integrates mined 3D lesion proposals back into the training data over multiple rounds.

Main Results:

  • The VFNet model achieved an average sensitivity of 46.9% at [0.125:8] false positives using only 30% of the data.
  • Performance is comparable to existing approaches that utilized the entire DeepLesion dataset.
  • The model successfully detected lesions in 3D and tagged them by body part.

Conclusions:

  • Self-training is an effective strategy for 3D lesion detection and tagging, even with limited data.
  • This approach can improve efficiency and accuracy in radiological assessments.
  • This work represents the first joint 3D lesion detection and body part tagging method.