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Liver segmentation from abdominal CT volumes based on level set and sparse shape composition.

Yang Li1, Yu-Qian Zhao2, Fan Zhang3

  • 1School of Automation, Central South University, Changsha 410083, China; School of Computer Science and Engineering, Changsha 410083, China; Hunan Engineering Research Center of High Strength Fastener Intelligent Manufacturing, Changde 415701, China.

Computer Methods and Programs in Biomedicine
|June 6, 2020
PubMed
Summary

This study introduces a novel framework for accurate liver segmentation in CT scans, effectively handling both healthy and pathological livers without extensive training. The method improves computer-aided surgery and liver disease diagnosis.

Keywords:
Graph cutLevel setLiver segmentationSparse shape composition

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Area of Science:

  • Medical Imaging
  • Computational Anatomy
  • Radiology

Background:

  • Accurate liver segmentation in abdominal CT is crucial for computer-aided surgery and disease diagnosis.
  • Challenges include intensity inhomogeneity and pathologies within liver CT volumes.
  • Existing methods struggle with accuracy in diverse liver conditions.

Purpose of the Study:

  • To present a novel framework for accurate liver segmentation from CT images.
  • To address challenges posed by intensity inhomogeneity and pathological variations in the liver.
  • To improve segmentation accuracy for both normal and abnormal liver structures.

Main Methods:

  • A level set method with intensity bias and position constraints for initial segmentation.
  • A sparse shape composition (SSC)-based method to refine pathological liver shapes.
  • An improved graph cut algorithm for further segmentation optimization.

Main Results:

  • The proposed method effectively segments both healthy and pathological livers on public datasets (SLIVER07, 3Dircadb).
  • Achieved mean accuracy metrics (e.g., ASD 0.9mm on SLIVER07) outperform existing methods.
  • Demonstrated robust performance across varied liver shapes and conditions.

Conclusions:

  • The novel framework provides satisfying and robust liver segmentation performance.
  • It does not require complex training on large datasets of liver samples.
  • The method is suitable for both normal and pathological liver segmentation in clinical applications.