간과 비장의 체적을 구하기 위한 3차원 영역 확장 기반 자동 영상 분할 알고리즘의 동물팬텀을 이용한 성능검증 = Evaluation of Automatic Image Segmentation for 3D Volume Measurement of Liver and Spleen Based on 3D Region-growing Algorithm using Animal Phantom

다국어 초록 (Multilingual 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.

참고문헌 (Reference)

1 Kashiwagi T, "Threedimensional demonstration of liver and spleen by a computer graphics technique" 29 : 27-31, 1988

2 Bae KT, "Pulmonary nodules: automated detection on CT images with morphologic matching algorithm--preliminary results" 236 : 286-293, 2005

3 Kawasaki S, "Preoperative measurement of segmental liver volume of donors for living related liver transplantation" 18 : 1115-1120, 1993

4 Kubota K, "Measurement of liver volume and hepatic functional reserve as a guide to decision- making in resectional surgery for hepatic tumors" 26 : 1176-1181, 1997

5 Lin XZ, "Liver, spleen and tumor volume measured by personal computer" 46 : 838-842, 1999

6 Hermoye L, "Liver segmentation in living liver transplant donors: comparison of semiautomatic and manual methods" 234 : 171-178, 2005

7 Sakamoto S, "Graft size assessment and analysis of donors for living donor liver transplantation sing right lobe" 71 : 1407-1413, 2001

8 Bellon E, "Evaluation of manual vs semi-automated delineation of liver lesions on CT images" 70 : 432-438, 1997

9 Seo KS, "Automatic liver segmentation of a contrast enhanced CT image using a prtial histogram threshold algorithm" 25 : 189-194, 2004

10 Kim JS, "Automated detection of pulmonary nodules on CT images: effect of section thickness and reconstruction interval--initial results" 236 : 295-299, 2005

11 Chen EL, "An automatic diagnostic system for CT liver image classification" 45 : 783-794, 1998

12 Nawaratne S, "Accuracy of volume measurement using helical CT" 21 : 481-486, 1997

13 Gao L, "Abdominal image segmentation using three-dimensional deformable models" 33 : 348-355, 1998

14 Ra SK, "3D medical image segmentation using region- grwing based tracking" 21 : 239-346, 2000