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Validation of a fully automated liver segmentation algorithm using multi-scale deep reinforcement learning and

David J Winkel1, Thomas J Weikert2, Hanns-Christian Breit2

  • 1Department of Radiology, University Hospital of Basel, Basel, Switzerland; Siemens Healthineers, Medical Imaging Technologies, Princeton, NJ, USA.

European Journal of Radiology
|March 16, 2020
PubMed
Summary

Artificial intelligence (AI) software accurately measures liver volumes, achieving excellent agreement with manual segmentation. This AI solution offers significant speed improvements for liver volumetric analysis in CT scans.

Keywords:
AlgorithmsArtificial intelligenceLiverReproducibility of resultsTomographyX-ray computed

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Area of Science:

  • Radiology
  • Medical Imaging
  • Artificial Intelligence in Medicine

Background:

  • Accurate liver volumetry is crucial for surgical planning and disease monitoring.
  • Manual segmentation of liver volumes from CT scans is time-consuming and subject to inter-observer variability.

Purpose of the Study:

  • To evaluate the performance of an artificial intelligence (AI) based software for liver volumetric analysis.
  • To compare AI-derived liver volumes against manual contour segmentation.

Main Methods:

  • Retrospective analysis of 462 multiphasic CT datasets (2772 series).
  • AI-based liver volumetry using multi-scale deep-reinforcement learning.
  • Comparison of AI results with manual segmentation by radiologists.

Main Results:

  • AI achieved a mean absolute error of 2.37% compared to manual segmentation.
  • AI processing time was significantly faster (9.94s) than manual segmentation (219.34s).
  • Excellent agreement was observed between AI and manual methods (ICC = 0.996).

Conclusions:

  • AI-powered automated liver volumetric analysis demonstrates high accuracy, reproducibility, and robustness.
  • The AI solution offers a significant speed advantage over manual segmentation.
  • AI shows excellent agreement with manual segmentation, supporting its clinical utility.