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Detecting the pulmonary trunk in CT scout views using deep learning.

Aydin Demircioğlu1, Magdalena Charis Stein2, Moon-Sung Kim3

  • 1Department of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, University of Duisburg-Essen, Hufelandstr. 55, 45147, Essen, Germany. aydin.demircioglu@uk-essen.de.

Scientific Reports
|May 14, 2021
PubMed
Summary
This summary is machine-generated.

We developed a deep learning method to automatically find the pulmonary trunk in CT scout views for better planning of CT pulmonary angiograms. This automated approach is highly accurate and performs comparably to human radiographers.

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence in Radiology
  • Deep Learning Applications

Background:

  • CT pulmonary angiograms require manual planning using scout views.
  • Radiographers manually identify the pulmonary trunk for bolus tracking, which is time-consuming and prone to variability.

Purpose of the Study:

  • To automate the localization of the pulmonary trunk in CT scout views using deep learning.
  • To evaluate the accuracy and performance of the automated method compared to radiographers.

Main Methods:

  • A U-Net deep learning model was trained on 620 annotated CT scout views.
  • The model predicted the region of the pulmonary trunk.
  • Performance was evaluated on 239 CT scout views and compared against three radiographers' annotations.

Main Results:

  • The deep learning network achieved 97.5% accuracy in localizing the pulmonary trunk region.
  • The automated method's selected slice position was, on average, 5.3 mm from the radiographer-defined reference standard.
  • The network's performance was non-inferior to that of three experienced radiographers (P < 0.001).

Conclusions:

  • Automated localization of the pulmonary trunk in CT scout views is feasible with high accuracy.
  • Deep learning offers a reliable and efficient alternative to manual localization by radiographers for CT pulmonary angiography planning.