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Automatic segmentation of thoracic CT images using three deep learning models.

D M Khalal1, A Behouch1, H Azizi1

  • 1Department of Physics, Faculty of Sciences, Laboratory of dosing, analysis and characterization in high resolution, Ferhat Abbas Sétif 1 University, El Baz campus, 19137 Sétif, Algeria.

Cancer Radiotherapie : Journal De La Societe Francaise De Radiotherapie Oncologique
|July 8, 2022
PubMed
Summary
This summary is machine-generated.

Deep learning models effectively segment thoracic organs at risk (OARs) and clinical target volumes (CTV) in CT scans. These U-Net based models show comparable performance, offering a promising alternative to manual segmentation.

Keywords:
Apprentissage profondAutomatic segmentationClinical target volumeDeep learningOrganes à risqueOrgans-at-riskSegmentation automatiqueVolume cible clinique

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Radiotherapy Planning

Background:

  • Manual segmentation of organs at risk (OARs) and clinical target volumes (CTV) in thoracic CT images is time-consuming and prone to variability.
  • Deep learning (DL) shows significant promise for automating medical image segmentation tasks.

Purpose of the Study:

  • To present and compare the segmentation results of OARs and CTV in thoracic CT images using three distinct DL models.
  • To evaluate the performance of DL-based segmentation against manual delineation and other existing models.

Main Methods:

  • Utilized CT images from 52 breast cancer patients.
  • Employed three U-Net architecture-based DL models for automatic segmentation of lungs, heart, and CTV.
  • Quantified segmentation accuracy using Dice Similarity Coefficient (DSC), Jaccard Index (J), and Hausdorff Distance (HD).

Main Results:

  • Presented DSC, J, and HD values for each segmented organ and model.
  • Visual comparisons of automatic segmentations against ground truth delineations were provided.
  • Performance metrics were benchmarked against recent studies by other researchers.

Conclusions:

  • The evaluated 2D DL models based on the U-Net architecture demonstrate good performance for delineating thoracic organs and CTV in CT images.
  • All three models exhibited similar performance levels, suggesting their viability for automated segmentation.
  • Further improvement in results is anticipated with larger datasets.