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Rapid Automated Target Segmentation and Tracking on 4D Data without Initial Contours

机译：无需初始轮廓即可对4D数据进行快速的自动目标分割和跟踪

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Purpose. To achieve rapid automated delineation of gross target volume (GTV) and to quantify changes in volume/position of the target for radiotherapy planning using four-dimensional (4D) CT.Methods and Materials. Novel morphological processing and successive localization (MPSL) algorithms were designed and implemented for achieving autosegmentation. Contours automatically generated using MPSL method were compared with contours generated using state-of-the-art deformable registration methods (usingElastix©and MIMVista software). Metrics such as the Dice similarity coefficient, sensitivity, and positive predictive value (PPV) were analyzed. The target motion tracked using the centroid of the GTV estimated using MPSL method was compared with motion tracked using deformable registration methods.Results. MPSL algorithm segmented the GTV in 4DCT images in27.0±11.1seconds per phase (512×512resolution) as compared to142.3±11.3seconds per phase for deformable registration based methods in 9 cases. Dice coefficients between MPSL generated GTV contours and manual contours (considered as ground-truth) were0.865±0.037. In comparison, the Dice coefficients between ground-truth and contours generated using deformable registration based methods were 0.909 ± 0.051.Conclusions. The MPSL method achieved similar segmentation accuracy as compared to state-of-the-art deformable registration based segmentation methods, but with significant reduction in time required for GTV segmentation.
机译：目的。为了实现对目标总体积（GTV）的快速自动描绘，并使用四维（4D）CT量化用于放射治疗计划的目标体积/位置的变化。方法和材料。设计并实现了新颖的形态学处理和连续定位（MPSL）算法，以实现自动分割。使用MPSL方法自动生成的轮廓与使用最新的可变形配准方法（使用Elastix®和MIMVista软件）生成的轮廓进行了比较。分析了诸如骰子相似性系数，敏感性和阳性预测值（PPV）等指标。将使用MPSL方法估计的，使用GTV质心跟踪的目标运动与使用可变形配准方法跟踪的运动进行了比较。 MPSL算法将4DCT图像中的GTV分割为每相27.0±11.1秒（512×512分辨率），而9种情况下基于可变形配准方法的每相142.3±11.3秒。 MPSL生成的GTV轮廓与手动轮廓之间的骰子系数（被认为是地面真相）为0.865±0.037。相比之下，基于可变形配准方法生成的地面真实性与轮廓之间的Dice系数为0.909±0.051。与最先进的基于可变形配准的分割方法相比，MPSL方法实现了类似的分割精度，但是大大减少了GTV分割所需的时间。

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