Department of Medical Radiation, Science and Research Branch, Islamic Azad University, Tehran, Iran.
Int J Radiat Oncol Biol Phys. 2013 Sep 1;87(1):195-201. doi: 10.1016/j.ijrobp.2013.04.049.
To evaluate the clinical application of a robust semiautomatic image segmentation method to determine the brain target volumes in radiation therapy treatment planning.
A local robust region-based algorithm was used on MRI brain images to study the clinical target volume (CTV) of several patients. First, 3 oncologists delineated CTVs of 10 patients manually, and the process time for each patient was calculated. The averages of the oncologists' contours were evaluated and considered as reference contours. Then, to determine the CTV through the semiautomatic method, a fourth oncologist who was blind to all manual contours selected 4-8 points around the edema and defined the initial contour. The time to obtain the final contour was calculated again for each patient. Manual and semiautomatic segmentation were compared using 3 different metric criteria: Dice coefficient, Hausdorff distance, and mean absolute distance. A comparison also was performed between volumes obtained from semiautomatic and manual methods.
Manual delineation processing time of tumors for each patient was dependent on its size and complexity and had a mean (±SD) of 12.33 ± 2.47 minutes, whereas it was 3.254 ± 1.7507 minutes for the semiautomatic method. Means of Dice coefficient, Hausdorff distance, and mean absolute distance between manual contours were 0.84 ± 0.02, 2.05 ± 0.66 cm, and 0.78 ± 0.15 cm, and they were 0.82 ± 0.03, 1.91 ± 0.65 cm, and 0.7 ± 0.22 cm between manual and semiautomatic contours, respectively. Moreover, the mean volume ratio (=semiautomatic/manual) calculated for all samples was 0.87.
Given the deformability of this method, the results showed reasonable accuracy and similarity to the results of manual contouring by the oncologists. This study shows that the localized region-based algorithms can have great ability in determining the CTV and can be appropriate alternatives for manual approaches in brain cancer.
评估一种强大的半自动图像分割方法在放射治疗计划中确定脑靶区体积的临床应用。
在 MRI 脑图像上使用局部鲁棒基于区域的算法研究了几个患者的临床靶区（CTV）。首先，三位肿瘤学家手动描绘了 10 名患者的 CTV，并计算了每位患者的过程时间。评估了肿瘤学家轮廓的平均值，并将其视为参考轮廓。然后，为了通过半自动方法确定 CTV，一位对所有手动轮廓均不知情的第四位肿瘤学家在水肿周围选择 4-8 个点，并定义初始轮廓。再次为每位患者计算获得最终轮廓的时间。使用 3 种不同的度量标准（Dice 系数、Hausdorff 距离和平均绝对距离）比较手动和半自动分割。还比较了半自动和手动方法获得的体积。
每位患者肿瘤的手动勾画处理时间取决于其大小和复杂性，平均值（±SD）为 12.33±2.47 分钟，而半自动方法为 3.254±1.7507 分钟。手动轮廓的 Dice 系数、Hausdorff 距离和平均绝对距离的平均值分别为 0.84±0.02、2.05±0.66cm 和 0.78±0.15cm，手动和半自动轮廓之间的平均值分别为 0.82±0.03、1.91±0.65cm 和 0.7±0.22cm。此外，所有样本的平均体积比（=半自动/手动）计算为 0.87。
鉴于该方法的可变形性，结果显示出与肿瘤学家手动轮廓相似的合理准确性。这项研究表明，局部基于区域的算法在确定 CTV 方面具有强大的能力，可以作为脑癌的手动方法的合适替代方案。
