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Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

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Published on: April 13, 2013

Liver Segmentation on CT and MR Using Laplacian Mesh Optimization.

Gabriel Chartrand, Thierry Cresson, Ramnada Chav

IEEE Transactions on Bio-Medical Engineering

|November 29, 2016

Summary

This summary is machine-generated.

A new semiautomated method accurately segments the liver in CT and MR images. This efficient tool aids in hepatic volumetry, virtual surgery, and treatment planning.

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Area of Science:

Medical Imaging
Computational Anatomy

Background:

Accurate liver segmentation is crucial for clinical applications like hepatic volumetry and surgical planning.
Manual segmentation is time-consuming and prone to inter-observer variability.

Purpose of the Study:

To introduce and evaluate a semiautomated method for liver segmentation.
To assess the method's performance on CT and MR images.

Main Methods:

Initialization of a 3D liver model using user contours.
Automatic deformation via Laplacian mesh optimization for precise delineation.
Inclusion of a user-guided correction tool for refinement.

Main Results:

Tested on 30 CT and 20 MR scans.
Achieved average volumetric overlap errors of 5.1% (CT) and 7.6% (MRI).
Average segmentation time was 6 minutes.

Conclusions:

The method is efficient and reliable for clinical use.
It simplifies the segmentation process, reducing manual effort.
Facilitates applications in virtual surgery and treatment planning.