Yuan Yading, Chao Ming, Sheu Ren-Dih, Rosenzweig Kenneth, Lo Yeh-Chi
Department of Radiation Oncology, Mount Sinai Hospital, Icahn School of Medicine at Mount Sinai, New York, New York 10029.
Med Phys. 2015 Jul;42(7):4015-26. doi: 10.1118/1.4922203.
This work aims to develop a robust and efficient method to track the fuzzy borders between liver and the abutted organs where automatic liver segmentation usually suffers, and to investigate its applications in automatic liver segmentation on noncontrast-enhanced planning computed tomography (CT) images.
In order to track the fuzzy liver-chestwall and liver-heart borders where oversegmentation is often found, a starting point and an ending point were first identified on the coronal view images; the fuzzy border was then determined as a geodesic curve constructed by minimizing the gradient-weighted path length between these two points near the fuzzy border. The minimization of path length was numerically solved by fast-marching method. The resultant fuzzy borders were incorporated into the authors' automatic segmentation scheme, in which the liver was initially estimated by a patient-specific adaptive thresholding and then refined by a geodesic active contour model. By using planning CT images of 15 liver patients treated with stereotactic body radiation therapy, the liver contours extracted by the proposed computerized scheme were compared with those manually delineated by a radiation oncologist.
The proposed automatic liver segmentation method yielded an average Dice similarity coefficient of 0.930 ± 0.015, whereas it was 0.912 ± 0.020 if the fuzzy border tracking was not used. The application of fuzzy border tracking was found to significantly improve the segmentation performance. The mean liver volume obtained by the proposed method was 1727 cm(3), whereas it was 1719 cm(3) for manual-outlined volumes. The computer-generated liver volumes achieved excellent agreement with manual-outlined volumes with correlation coefficient of 0.98.
The proposed method was shown to provide accurate segmentation for liver in the planning CT images where contrast agent is not applied. The authors' results also clearly demonstrated that the application of tracking the fuzzy borders could significantly reduce contour leakage during active contour evolution.
本研究旨在开发一种强大且高效的方法，用于追踪肝脏与相邻器官之间模糊的边界（自动肝脏分割通常在此处遇到困难），并研究其在非增强计划计算机断层扫描（CT）图像上的自动肝脏分割中的应用。
为了追踪经常出现过分割的模糊肝脏 - 胸壁和肝脏 - 心脏边界，首先在冠状视图图像上确定一个起点和一个终点；然后将模糊边界确定为由快速行进法通过最小化这两个点在模糊边界附近的梯度加权路径长度而构建的测地线曲线。路径长度的最小化通过快速行进法进行数值求解。所得的模糊边界被纳入作者的自动分割方案中，其中肝脏最初通过患者特异性自适应阈值化进行估计，然后通过测地线活动轮廓模型进行细化。通过使用15例接受立体定向体部放射治疗的肝脏患者的计划CT图像，将所提出的计算机化方案提取的肝脏轮廓与放射肿瘤学家手动勾勒的轮廓进行比较。
所提出的自动肝脏分割方法产生的平均骰子相似系数为0.930±0.015，而如果不使用模糊边界追踪，该系数为0.912±0.020。发现模糊边界追踪的应用显著提高了分割性能。所提出的方法获得的平均肝脏体积为1727 cm³，而手动勾勒的体积为1719 cm³。计算机生成的肝脏体积与手动勾勒的体积具有极好的一致性，相关系数为0.98。
所提出的方法被证明在未应用造影剂的计划CT图像中能够为肝脏提供准确的分割。作者的结果还清楚地表明，追踪模糊边界的应用可以在活动轮廓演变过程中显著减少轮廓泄漏。
