Özsavaş Emin Emrah, Telatar Ziya, Dirican Bahar, Sağer Ömer, Beyzadeoğlu Murat
Electrical and Electronics Engineering Department, Faculty of Engineering, Ankara University, Gölbaşı, 06830 Ankara, Turkey.
Radiation Oncology Department, Gülhane Military Medical Academy, Etlik, 06018 Ankara, Turkey.
Comput Math Methods Med. 2014;2014:472890. doi: 10.1155/2014/472890. Epub 2014 Dec 18.
Modern radiotherapy techniques are vulnerable to delineation inaccuracies owing to the steep dose gradient around the target. In this aspect, accurate contouring comprises an indispensable part of optimal radiation treatment planning (RTP). We suggest a fully automated method to segment the lungs, trachea/main bronchi, and spinal canal accurately from computed tomography (CT) scans of patients with lung cancer to use for RTP. For this purpose, we developed a new algorithm for inclusion of excluded pathological areas into the segmented lungs and a modified version of the fuzzy segmentation by morphological reconstruction for spinal canal segmentation and implemented some image processing algorithms along with them. To assess the accuracy, we performed two comparisons between the automatically obtained results and the results obtained manually by an expert. The average volume overlap ratio values range between 94.30 ± 3.93% and 99.11 ± 0.26% on the two different datasets. We obtained the average symmetric surface distance values between the ranges of 0.28 ± 0.21 mm and 0.89 ± 0.32 mm by using the same datasets. Our method provides favorable results in the segmentation of CT scans of patients with lung cancer and can avoid heavy computational load and might offer expedited segmentation that can be used in RTP.
由于靶区周围剂量梯度陡峭，现代放射治疗技术容易受到轮廓勾画不准确的影响。在这方面，精确的轮廓勾画是优化放射治疗计划（RTP）不可或缺的一部分。我们提出了一种全自动方法，可从肺癌患者的计算机断层扫描（CT）图像中准确分割出肺、气管/主支气管和椎管，用于放射治疗计划。为此，我们开发了一种新算法，将排除的病理区域纳入分割的肺中，并对用于椎管分割的形态学重建模糊分割进行了改进，同时还实施了一些图像处理算法。为了评估准确性，我们将自动获得的结果与专家手动获得的结果进行了两次比较。在两个不同的数据集中，平均体积重叠率值在94.30±3.93%至99.11±0.26%之间。使用相同的数据集，我们获得的平均对称表面距离值在0.28±0.21毫米至0.89±0.32毫米之间。我们的方法在肺癌患者CT图像分割中取得了良好的结果，可避免繁重的计算负担，并可能提供可用于放射治疗计划的快速分割。
