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Automatic liver segmentation method featuring a novel filter for multiphase multidetector-row helical computed

Tomohiro Hirose¹, Norihisa Nitta, Masaru Tsudagawa

¹Department of Radiology, Shiga University of Medical Science, Otsu City, Shiga, Japan. tomohiro.hirose@nifty.com

Journal of Computer Assisted Tomography

|May 19, 2011

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This study introduces an automatic liver segmentation method using a novel adaptive linear prediction filter for multiphase multidetector-row helical computed tomography, achieving high accuracy.

Area of Science:

Medical Imaging
Radiology
Computer-Aided Diagnosis

Background:

Accurate liver segmentation is crucial for quantitative analysis in medical imaging.
Manual segmentation is time-consuming and prone to inter-observer variability.
Automated methods are needed to improve efficiency and reproducibility.

Purpose of the Study:

To develop and evaluate a novel automatic liver segmentation method.
To incorporate an adaptive linear prediction filter for enhanced accuracy.
To apply the method to multiphase multidetector-row helical computed tomography (MDCT) scans.

Main Methods:

Acquisition of 3-phase MDCT scans (unenhanced, arterial, portal).
Development of a novel adaptive linear prediction filter.

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Segmentation of liver regions using the developed filter.

Main Results:

The segmentation algorithm achieved a high mean Dice Similarity Coefficient (DSC) of 91.4%.
The adaptive linear prediction filter effectively differentiated liver from surrounding tissues.
The method demonstrated robust performance across different phases of MDCT.

Conclusions:

The novel adaptive linear prediction filter is effective for automatic liver segmentation.
This method offers a promising tool for quantitative liver analysis in radiology.
The automated approach enhances efficiency and accuracy in medical image analysis.