Automatic segmentation and determining radiodensity of the liver in a large-scale CT database

TL;DR

This paper introduces an automatic liver segmentation method for large-scale CT databases that uses template correlation for localization and calculates radiodensity to identify abnormalities, demonstrating applicability to diverse CT scans.

Contribution

The study presents a novel automatic liver segmentation technique based on template correlation, capable of processing large-scale, low-dose CT datasets with varied patient positioning.

Findings

Effective on 700 CT images from URIS dataset

Applicable to low-dose and partially visible liver regions

Suitable for large-scale medical image analysis

Abstract

This study proposes an automatic technique for liver segmentation in computed tomography (CT) images. Localization of the liver volume is based on the correlation with an optimized set of liver templates developed by the authors that allows clear geometric interpretation. Radiodensity values are calculated based on the boundaries of the segmented liver, which allows identifying liver abnormalities. The performance of the technique was evaluated on 700 CT images from dataset of the Unified Radiological Information System (URIS) of Moscow. Despite the decrease in accuracy, the technique is applicable to CT volumes with a partially visible region of the liver. The technique can be used to process CT images obtained in various patient positions in a wide range of exposition parameters. It is capable in dealing with low dose CT scans in real large-scale medical database with over 1 million of studies.