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Combining watershed and graph cuts methods to segment organs at risk in radiotherapy

机译:分水岭和图割相结合的方法来分割放疗中有风险的器官

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Computer-aided segmentation of anatomical structures in medical images is a valuable tool for efficient radiation therapy planning (RTP). As delineation errors highly affect the radiation oncology treatment, it is crucial to delineate geometric structures accurately. In this paper, a semi-automatic segmentation approach for computed tomography (CT) images, based on watershed and graph-cuts methods, is presented. The watershed pre-segmentation groups small areas of similar intensities in homogeneous labels, which are subsequently used as input for the graph-cuts algorithm. This methodology does not require of prior knowledge of the structure to be segmented; even so, it performs well with complex shapes and low intensity. The presented method also allows the user to add foreground and background strokes in any of the three standard orthogonal views - axial, sagittal or coronal - making the interaction with the algorithm easy and fast. Hence, the segmentation information is propagated within the whole volume, providing a spatially coherent result. The proposed algorithm has been evaluated using 9 CT volumes, by comparing its segmentation performance over several organs -lungs, liver, spleen, heart and aorta - to those of manual delineation from experts. A Dice's coefficient higher than 0.89 was achieved in every case. That demonstrates that the proposed approach works well for all the anatomical structures analyzed. Due to the quality of the results, the introduction of the proposed approach in the RTP process will be a helpful tool for organs at risk (OARs) segmentation.
机译:医学图像中解剖结构的计算机辅助分割是有效放射治疗计划(RTP)的宝贵工具。由于划定误差严重影响放射肿瘤学治疗,因此准确划定几何结构至关重要。本文提出了一种基于分水岭和图割方法的计算机断层扫描(CT)图像半自动分割方法。分水岭预先分割后,在均质标签中将强度相似的小区域分组,然后将其用作图割算法的输入。这种方法不需要对要分割的结构有先验知识;即使这样,它在复杂形状和低强度下也能表现良好。所提出的方法还允许用户在轴向,矢状或冠状的三个标准正交视图中的任何一个中添加前景和背景笔划,从而使与算法的交互变得容易且快速。因此,分割信息在整个体积内传播,从而提供空间上连贯的结果。通过比较9种CT量对拟议算法的分割性能,并将其在多个器官(肺,肝,脾,心脏和主动脉)上的分割性能与专家的手动划界进行了比较,来评估该算法。在每种情况下都可获得高于0.89的骰子系数。这证明了所提出的方法对于所分析的所有解剖结构都适用。由于结果的质量,在RTP流程中引入建议的方法将对危险器官(OARs)分割是一个有用的工具。

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