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Multiorgan segmentation using distance-aware adversarial networks.

Roger Trullo1, Caroline Petitjean1, Bernard Dubray2

  • 1Normandie University, Institut National des Sciences Appliquées Rouen, LITIS, Rouen, France.

Journal of Medical Imaging (Bellingham, Wash.)
|January 22, 2019
PubMed
Summary
This summary is machine-generated.

This study introduces an automated framework for segmenting organs at risk (OAR) in CT scans, improving radiotherapy planning. The novel approach uses a distance map for organ localization, enhancing segmentation accuracy.

Keywords:
convolutional neural networksdeep learningdistance mapgenerative adversarial networksmedical imagesmultiorgansegmentation

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Area of Science:

  • Medical Imaging
  • Radiotherapy
  • Artificial Intelligence

Background:

  • Accurate segmentation of organs at risk (OAR) in computed tomography (CT) is crucial for effective radiotherapy.
  • Manual segmentation is time-consuming and challenging for organs with low contrast.

Purpose of the Study:

  • To develop an automated framework for segmenting multiple OAR (esophagus, heart, trachea, aorta) in CT scans.
  • To improve the efficiency and accuracy of OAR segmentation in radiotherapy planning.

Main Methods:

  • A novel framework utilizing global localization information via a distance map to determine organ locations and spatial relationships.
  • Generation of a localization map using an adversarial framework to minimize reconstruction error.
  • A fully convolutional network guided by the localization map for organ segmentation.

Main Results:

  • The proposed framework demonstrated encouraging performance in segmenting multiple OAR on CT scans from 60 patients (11,084 slices).
  • Results showed significant improvements compared to existing state-of-the-art methods.

Conclusions:

  • The developed framework offers an effective solution for automatic OAR segmentation in CT, aiding radiotherapy.
  • The integration of global localization information improves segmentation accuracy and efficiency.