Semiautomated Segmentation of Kidney From High-Resolution Multidetector Computed Tomography Images Using a Graph-Cuts Technique
Semiautomated Segmentation of Kidney From High-Resolution Multidetector Computed Tomography Images Using a Graph-Cuts Technique
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DOI：
10.1097/rct.0b013e3181a5cc16
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发表时间：
2009-11-01
影响因子：
1.3
通讯作者：
Bae, Kyongtae T.
中科院分区：
文献类型：
--
作者：
Shim, Hackjoon;Chang, Samuel;Bae, Kyongtae T.
目的：开发一个半自动分割方法的基础上的图形切割技术从多探测器计算机断层扫描图像肾脏分割和评估和比较它与传统的手动划定segmentation method.Materials和方法：我们已经开发了一个半自动分割方法，这是基于图形切割技术与增强功能，包括自动种子生长。多探测器计算机断层扫描图像从15个连续的患者谁正在被评估为可能的活体供肾移植。两名观察员独立进行分割的肾脏从多探测器计算机断层扫描图像使用手动和半自动化的方法。通过分割处理时间来衡量两种方法的效率，并进行比较。通过Dice相似系数（DSC）确定观察者间和方法的再现性，DSC测量2个分割体积几何重叠的紧密程度和体积测量的变异系数。平均分割处理时间为（手动vs半自动，P < 0.001）观察者1为96.8 +/- 13.6 vs 13.7 +/- 3.5分钟，观察者2为44.3 +/- 4.7 vs 16.2 +/- 5.1分钟。DSC的平均观察者间重现性为（手动vs半自动，P < 0.001）916 1.6% vs 97.3 +/- 0.9%，变异系数为5.3 +/- 2.6% vs 2.2 +/- 1.3%，表明半自动方法的观察者间重现性高于手动方法。两种分割方法之间的协议是高的（平均intermethod DSC 95.8 +/- 1.0%和94.9 +/- 0.8%）为both observers.Conclusions：半自动化的方法是显着更有效的和可重复的比手动划定方法从MDCT图像分割肾脏。
Objectives: To develop a semiautomated segmentation method based on a graph-cuts technique from multidetector computed tomography images for kidney segmentation and to evaluate and compare it with the conventional manual delineation segmentation method.Materials and Methods: We have developed a semiautomated segmentation method that is based on a graph-cuts technique with enhanced features including automated seed growing. Multidetector computed tomography images were obtained from 15 consecutive patients who were being evaluated as possible living donors for kidney transplant. Two observers independently performed the segmentation of the kidney from the multidetector computed tomography images using the manual and semiautomated methods. The efficiency of the 2 methods were measured by segmentation processing times and then compared. The interobserver and method reproducibility was determined by Dice similarity coefficient (DSC), which measures how closely 2 segmented volumes overlap geometrically and the coefficient of variation of volume measurements.Results: The mean segmentation processing time was (manual vs semiautomated, P < 0.001) 96.8 +/- 13.6 vs 13.7 +/- 3.5 minutes for observer 1 and 44.3 +/- 4.7 vs 16.2 +/- 5.1 minutes for observer 2. The mean interobserver reproducibility was (manual vs semiautomated, P < 0.001) 916 1.6% vs 97.3 +/- 0.9% for DSC and 5.3 +/- 2.6% vs 2.2 +/- 1.3% for coefficient of variation, indicating higher interobserver reproducibility with the semiautomated than manual method. The agreement between the 2 segmentation methods was high (mean intermethod DSC 95.8 +/- 1.0% and 94.9 +/- 0.8%) for both observers.Conclusions: The semiautomated method was significantly more efficient and reproducible than the manual delineation method for segmentation of kidney from MDCT images.