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Automatic cervical lymphadenopathy segmentation from CT data using deep learning.

Adele Courot1, Diana L F Cabrera2, Nicolas Gogin1

  • 1General Electric Healthcare, 78530 Buc, France.

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Summary
This summary is machine-generated.

This study developed a fast algorithm using convolutional neural networks (CNNs) to automatically detect and segment lymphadenopathy in head and neck CT scans, showing promising results for clinical use.

Keywords:
Artificial intelligenceComputer-assistedDeep learningImage processingLymphadenopathyTomographyX-ray computed

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Oncology

Background:

  • Lymphadenopathy detection in head and neck cancers is crucial for diagnosis and treatment planning.
  • Manual segmentation of lymph nodes on CT scans is time-consuming and subject to inter-observer variability.

Purpose of the Study:

  • To develop a fast and automatic algorithm for lymphadenopathy detection and segmentation.
  • To improve the efficiency and accuracy of lymph node assessment in head and neck CT examinations.

Main Methods:

  • An ensemble of three U-Net based convolutional neural networks (CNNs) was trained in a fully supervised manner.
  • The algorithm was trained on 117 annotated CT acquisitions and validated on 150 additional scans.
  • Performance was evaluated using the Dice similarity coefficient (DSC) and a custom formula for non-adenopathic cases.

Main Results:

  • The developed algorithm achieved a mean performance score of 0.63 on an independent test set.
  • The CNN-based approach demonstrated promising results in segmenting cervical lymphadenopathy.
  • The system showed potential for precise quantification in clinical workflows.

Conclusions:

  • The CNN-based algorithm shows potential for assisting clinicians in lymphadenopathy detection and quantification.
  • Despite limitations in data and annotations, the approach achieved promising segmentation results.
  • The developed tool could enhance the clinical workflow for head and neck cancer management.