Evaluation of Automatic Image Segmentation for 3D Volume Measurement of Liver and Spleen Based on 3D Region-growing Algorithm using Animal Phantom.
- Author:
Jin Sung KIM
1
;
June Sik CHO
;
Kyung Sook SHIN
;
Jin Hwan KIM
;
Hosang JEON
;
Gyuseong CHO
Author Information
1. Proton Therapy Center, National Cancer Center, Korea.
- Publication Type:Original Article
- Keywords:
Liver segmentation;
Volume measurement;
Phantom;
Computed aided detection
- MeSH:
Animals;
Cadaver;
Dietary Sucrose;
Humans;
Liver;
Liver Transplantation;
Living Donors;
Magnetic Resonance Spectroscopy;
Mass Screening;
Mathematics;
Spleen;
Tissue Donors;
Transplants
- From:Korean Journal of Medical Physics
2008;19(3):178-185
- CountryRepublic of Korea
- Language:Korean
-
Abstract:
Living donor liver transplantation is increasingly performed as an alternative to cadaveric transplantation. Preoperative screening of the donor candidates is very important. The quality, size, and vascular and biliary anatomy of the liver are best assessed with magnetic resonance (MR) imaging or computed tomography (CT). In particular, the volume of the potential graft must be measured to ensure sufficient liver function after surgery. Preoperative liver segmentation has proved useful for measuring the graft volume before living donor liver transplantations in previous studies. In these studies, the liver segments were manually delineated on each image section. The delineated areas were multiplied by the section thickness to obtain volumes and summed to obtain the total volume of the liver segments. This process is tedious and time consuming. To compensate for this problem, automatic segmentation techniques have been proposed with multiplanar CT images. These methods involve the use of sequences of thresholding, morphologic operations (ie, mathematic operations, such as image dilation, erosion, opening, and closing, that are based on shape), and 3D region growing methods. These techniques are complex but require a few computation times. We made a phantom for volume measurement with pig and evaluated actual volume of spleen and liver of phantom. The results represent that our semiautomatic volume measurement algorithm shows a good accuracy and repeatability with actual volume of phantom and possibility for clinical use to assist physician as a measuring tool.
