首页> 外文会议>Prostate cancer imaging : Computer-aided diagnosis, prognosis, and intervention >Atlas Based Segmentation and Mapping of Organs at Risk from Planning CT for the Development of Voxel-Wise Predictive Models of Toxicity in Prostate Radiotherapy
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Atlas Based Segmentation and Mapping of Organs at Risk from Planning CT for the Development of Voxel-Wise Predictive Models of Toxicity in Prostate Radiotherapy

机译:基于Atlas的前列腺癌放射治疗的Voxel-Wise预测毒性模型预测CT计划中处于危险中的器官分割和绘图

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The prediction of toxicity is crucial to managing prostate cancer ra diotherapy (RT). This prediction is classically organ wise and based on the dose volume histograms (DVH) computed during the planning step, and using for example the mathematical Lyman Normal Tissue Complication Probability (NTCP) model. However, these models lack spatial accuracy, do not take into account deformations and may be inappropiate to explain toxicity events related with the distribution of the delivered dose. Producing voxel wise statistical models of toxicity might help to explain the risks linked to the dose spatial distribution but is challenging due to the difficulties lying on the mapping of organs and dose in a common template. In this paper we investigate the use of atlas based methods to perform the non-rigid mapping and segmentation of the individuals' organs at risk (OAR) from CT scans. To build a labeled atlas, 19 CT scans were selected from a population of patients treated for prostate cancer by radiotherapy. The prostate and the OAR (Rectum, Bladder, Bones) were then manually delineated by an expert and constituted the training data. After a number of affine and non rigid registration iterations, an average image (template) representing the whole population was obtained. The amount of consensus between labels was used to gener ate probabilistic maps for each organ. We validated the accuracy of the approach by segmenting the organs using the training data in a leave one out scheme. The agreement between the volumes after deformable registration and the manually segmented organs was on average above 60% for the organs at risk. The proposed methodology provides a way to map the organs from a whole population on a single template and sets the stage to perform further voxel wise analysis. With this method new and accurate predictive models of toxicity will be built.
机译:毒性的预测对于管理前列腺癌放疗(RT)至关重要。该预测经典地是器官明智的,并且基于在计划步骤期间计算的剂量体积直方图(DVH),并且使用例如数学上的莱曼正常组织并发症概率(NTCP)模型。但是,这些模型缺乏空间准确性,没有考虑变形,可能不适合解释与所输送剂量的分布有关的毒性事件。产生毒性的体素明智的统计模型可能有助于解释与剂量空间分布有关的风险,但由于在通用模板中绘制器官和剂量的困难而具有挑战性。在本文中,我们研究了使用基于图集的方法对来自CT扫描的个体处于危险状态的器官(OAR)进行非刚性映射和分割的情况。为了建立标记图集,从通过放射疗法治疗前列腺癌的患者人群中选择了19项CT扫描。然后由专家手动描绘前列腺和OAR(直肠,膀胱,骨骼),并构成训练数据。经过多次仿射和非刚性配准迭代后,获得了代表整个总体的平均图像(模板)。标签之间的共有量用于生成每个器官的概率图。我们通过采用留一法的训练数据对器官进行分割来验证该方法的准确性。对于有风险的器官,可变形套准后的体积与手动分割的器官之间的一致性平均高于60%。所提出的方法提供了一种在单个模板上绘制整个人群的器官图的方法,并为进一步进行体素分析提供了条件。用这种方法,将建立新的和准确的毒性预测模型。

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