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Semi-automatic liver segmentation based on probabilistic models and anatomical constraints.

Doan Cong Le1, Krisana Chinnasarn2, Jirapa Chansangrat3

  • 1School of Computer Engineering, Institute of Engineering, Suranaree University of Technology, Nakhon Ratchasima, Thailand.

Scientific Reports
|March 18, 2021
PubMed
Summary

This study introduces a semi-automatic method for segmenting livers in CT scans, improving accuracy and reliability for computer-aided diagnosis. The approach uses probabilistic models and graph-cut optimization with minimal user interaction.

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Area of Science:

  • Medical Imaging
  • Computer-Aided Diagnosis
  • Image Segmentation

Background:

  • Accurate liver segmentation from abdominal computed tomography (CT) is vital for computer-aided diagnosis and treatment planning.
  • Challenges in liver segmentation include indistinct boundaries, intensity variations, and patient-specific anatomical differences.
  • Existing automated methods often struggle with precision due to these inherent complexities.

Purpose of the Study:

  • To develop a semi-automatic method for precise liver segmentation in abdominal CT scans.
  • To minimize human interaction while ensuring accurate and reliable liver delineation.
  • To enhance the accuracy and robustness of liver segmentation for clinical applications.

Main Methods:

  • A semi-automatic segmentation approach combining multivariable normal distribution of liver tissues with graph-cut sub-division.
  • Creation of a subject-specific probabilistic model using user-defined seed points and interior patches.
  • Iterative pixel label assignment incorporating spatio-contextual information and graph-cut optimization for 3D liver extraction.
  • Post-processing using bottleneck detection and adjacent contour constraints to refine segmentation accuracy.

Main Results:

  • The proposed system demonstrated improved accuracy and reliability in asymptomatic liver segmentation.
  • Validation on the MICCAI SLIVER07 dataset showed competitive performance against state-of-the-art methods.
  • Both visual and quantitative assessments confirmed the system's effectiveness in handling challenging segmentation scenarios.

Conclusions:

  • The developed semi-automatic method offers a reliable solution for liver segmentation in CT imaging.
  • Minimal user interaction combined with advanced image processing techniques enhances segmentation precision.
  • This approach holds potential for improving diagnostic and therapeutic interventions in clinical practice.