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Automatic Scan Range Delimitation in Chest CT Using Deep Learning.

Aydin Demircioğlu¹, Moon-Sung Kim¹, Magdalena Charis Stein¹

¹Department of Diagnostic and Interventional Radiology and Neuroradiology (A.D., M.S.K., M.C.S., L.U., K.N.) and Department of Radiotherapy (N.G.), University Hospital Essen, University of Duisburg-Essen, Hufelandstrasse 55, D-45147 Essen, Germany.

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|June 17, 2021

Summary

This summary is machine-generated.

Deep learning accurately automates chest CT scan range delimitation. This method reduces scan times and radiation exposure, enhancing patient safety and efficiency in medical imaging.

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Area of Science:

Radiology and Medical Imaging
Artificial Intelligence in Healthcare
Deep Learning Applications

Background:

Accurate scan range delimitation in chest CT is crucial for diagnostic quality and radiation dose optimization.
Current clinical routines may involve suboptimal scan ranges, potentially increasing radiation exposure.
Automating this process can improve efficiency and consistency.

Purpose of the Study:

To develop and evaluate a fully automatic deep learning-based method for chest CT scan range delimitation.
To assess the accuracy and efficiency of the automated system compared to expert annotations and clinical routine.

Main Methods:

A retrospective study utilizing 1149 chest CT topograms.
Training a conditional generative adversarial neural network (CNN) on 1000 topograms for virtual scan range generation.
Comparing software-based delimitations with expert annotations and clinical routine on 149 topograms using Dice scores and equivalence tests.

Main Results:

The automated system achieved high accuracy, with a mean Dice score of 0.99 ± 0.01 compared to expert annotations.
Software-based scan ranges were significantly shorter than those in clinical routine (298.2 mm vs 327.0 mm).
This resulted in a lower simulated total radiation exposure (3.9 mSv vs 4.2 mSv).

Conclusions:

A conditional generative adversarial neural network effectively automates chest CT scan range delimitation with high accuracy.
The automated method shows potential for reducing scan times and radiation exposure.
This deep learning approach offers a promising tool for optimizing chest CT protocols.