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Improving airway segmentation in computed tomography using leak detection with convolutional networks.

Jean-Paul Charbonnier1, Eva M van Rikxoort1, Arnaud A A Setio1

  • 1Diagnostic Image Analysis Group, Radboud University Medical Center, Geert Grooteplein 10, 6525 GA, Nijmegen, The Netherlands.

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Summary

This study introduces a new method using convolutional neural networks (ConvNets) to remove leaks in airway segmentation from CT scans. This approach enhances airway tree length and accuracy without extensive parameter tuning.

Keywords:
Airway segmentationChest computed tomographyConvolutional networks

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Area of Science:

  • Medical Imaging
  • Computer Vision
  • Pulmonary Medicine

Background:

  • Accurate airway segmentation in thoracic computed tomography (CT) is crucial for diagnosing and managing respiratory diseases.
  • Existing airway segmentation algorithms often produce "leaky" segmentations, limiting their clinical utility.
  • Parameter fine-tuning of segmentation algorithms is time-consuming and may not fully address segmentation inaccuracies.

Purpose of the Study:

  • To develop and validate a novel method for improving airway segmentation in thoracic CT scans.
  • To effectively detect and remove leaks from airway segmentations.
  • To increase the segmented airway tree length while minimizing segmentation errors.

Main Methods:

  • Leak detection formulated as a supervised classification problem using a convolutional neural network (ConvNet).
  • Strategy involving combining multiple segmentations generated by varying algorithm parameters, followed by leak removal.
  • Training and evaluation on inspiratory thoracic CT scans from the COPDGene study.
  • Validation on the independent EXACT'09 challenge dataset.

Main Results:

  • The proposed method significantly improves the quality of leaky airway segmentations.
  • Achieved higher sensitivity with a low false-positive rate compared to state-of-the-art methods in the EXACT'09 challenge.
  • The leak removal strategy effectively increases segmented airway tree length without extensive parameter tuning.

Conclusions:

  • The ConvNet-based leak detection and removal method offers a robust solution for enhancing airway segmentation accuracy.
  • This approach circumvents the need for laborious parameter fine-tuning of segmentation algorithms.
  • The method demonstrates superior performance, approaching the combined results of multiple algorithms in a competitive challenge.