Shim Hackjoon, Chang Samuel, Tao Cheng, Wang Jin Hong, Kaya Diana, Bae Kyongtae T
School of Electrical Engineering, Seoul National University, Seoul, Republic of Korea.
J Comput Assist Tomogr. 2009 Nov-Dec;33(6):893-901. doi: 10.1097/RCT.0b013e3181a5cc16.
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.
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.
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) 93.6 +/- 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.
The semiautomated method was significantly more efficient and reproducible than the manual delineation method for segmentation of kidney from MDCT images.
基于图割技术开发一种用于多排螺旋计算机断层扫描（MDCT）图像肾脏分割的半自动分割方法，并将其与传统的手动描绘分割方法进行评估和比较。
我们开发了一种基于图割技术的半自动分割方法，该方法具有包括自动种子生长在内的增强特征。从15名连续接受肾脏移植活体供体评估的患者中获取多排螺旋计算机断层扫描图像。两名观察者分别使用手动和半自动方法从多排螺旋计算机断层扫描图像中对肾脏进行分割。通过分割处理时间来衡量这两种方法的效率，然后进行比较。通过测量两个分割体积在几何上的重叠程度的骰子相似系数（DSC）以及体积测量的变异系数来确定观察者间和方法的可重复性。
观察者1的平均分割处理时间为（手动与半自动，P < 0.001）96.8±13.6分钟对13.7±3.5分钟，观察者2为44.3±4.7分钟对16.2±5.1分钟。观察者间平均可重复性为（手动与半自动，P < 0.001），DSC为93.6±1.6%对97.3±0.9%，变异系数为5.3±2.6%对2.2±1.3%，表明半自动方法的观察者间可重复性高于手动方法。两位观察者的两种分割方法之间的一致性都很高（平均方法间DSC为95.8±1.0%和94.9±0.8%）。
对于从MDCT图像中分割肾脏，半自动方法比手动描绘方法显著更高效且可重复。
