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Updated: May 15, 2026

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images
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Liver segmentation approach using graph cuts and iteratively estimated shape and intensity constrains.

Ahmed Afifi1, Toshiya Nakaguchi

  • 1Faculty of Computers and Information, Menoufia University, Egypt.

Medical Image Computing and Computer-Assisted Intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
|January 5, 2013
PubMed
Summary

This study introduces an automated liver segmentation method using CT images. It efficiently segments the liver by leveraging neighboring slice information and graph cuts, reducing manual effort and processing time.

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Area of Science:

  • Medical Imaging
  • Computer-Aided Diagnosis
  • Radiology

Background:

  • Accurate liver segmentation is crucial for diagnosing and monitoring liver diseases.
  • Existing methods often require extensive manual input or complex prior models.

Purpose of the Study:

  • To develop an automated liver segmentation approach for CT images.
  • To reduce user interaction and processing time compared to current methods.

Main Methods:

  • Utilizes the relationship between neighboring CT slices to infer liver shape and statistics.
  • Integrates this information with the graph cuts algorithm for segmentation.
  • Requires minimal user interaction, involving landmark selection on a single slice.

Main Results:

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  • Achieved an average score of 81.7% on the MICCAI-2007 Grand Challenge scoring system.
  • Successfully segmented livers in CT images with abnormalities like tumors and cysts.
  • Demonstrated robustness to complex shape and intensity variations.

Conclusions:

  • The proposed method offers an efficient and less interactive approach to liver segmentation.
  • It effectively handles variations in liver shape and intensity without requiring prior models.
  • This technique shows promise for improving clinical workflows in liver imaging analysis.