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Laparoscopic Anatomical Liver Segment VII Resection with Liver Parenchymal Transection Following a Priority Approach
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Fully automatic liver attenuation estimation combing CNN segmentation and morphological operations.

Yuankai Huo1, James G Terry2, Jiachen Wang1

  • 1Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN, 37235, USA.

Medical Physics
|June 23, 2019
PubMed
Summary
This summary is machine-generated.

The automated liver attenuation ROI-based measurement (ALARM) method accurately estimates hepatic steatosis using deep learning for nonalcoholic fatty liver disease (NAFLD) diagnosis. This automated approach offers excellent agreement with manual measurements, improving efficiency in CT-based liver analysis.

Keywords:
AADHSdeep learningdeep neural networkdockerfatty liverliver attenuationliver segmentation

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Area of Science:

  • Radiology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Manual tracing of liver regions of interest (ROIs) is standard for measuring liver attenuation on CT scans to diagnose nonalcoholic fatty liver disease (NAFLD).
  • Manual tracing is time-consuming and resource-intensive, limiting widespread quantitative CT use for hepatic steatosis assessment.

Purpose of the Study:

  • To develop and validate an automated method, the ALARM (automated liver attenuation ROI-based measurement) method, for estimating liver attenuation.
  • To address the limitations of manual ROI tracing in NAFLD diagnosis.

Main Methods:

  • The ALARM method utilizes a deep convolutional neural network (DCNN) for automated liver segmentation.
  • Automated ROIs (center-ROI and periphery-ROI) are extracted using morphological operations based on the segmented liver.
  • The method is implemented as an open-source Docker container.

Main Results:

  • The ALARM method demonstrated high correlation (Pearson r=0.94) with manual liver attenuation estimations.
  • For NAFLD detection (cut-off < 40 HU), the periphery-ROI achieved "excellent" agreement (Kappa=0.88) and center-ROI achieved substantial agreement (Kappa=0.79) with manual estimations.
  • The automated method showed reduced variability compared to manual measurements.

Conclusions:

  • The proposed ALARM method, combining DCNN and morphological operations, provides fully automated liver attenuation estimation.
  • ALARM achieves excellent agreement with manual methods for fatty liver detection and can process a CT exam in approximately five minutes.