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首页> 外文期刊>Radiotherapy and oncology: Journal of the European Society for Therapeutic Radiology and Oncology >A qualitative and a quantitative analysis of an auto-segmentation module for prostate cancer.
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A qualitative and a quantitative analysis of an auto-segmentation module for prostate cancer.

机译:前列腺癌自动细分模块的定性和定量分析。

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PURPOSE: This work describes the clinical validation of an automatic segmentation algorithm in CT-based radiotherapy planning for prostate cancer patients. MATERIAL AND METHODS: The validated auto-segmentation algorithm (Smart Segmentation, version 1.0.05) is a rule-based algorithm using anatomical reference points and organ-specific segmentation methods, developed by Varian Medical Systems (Varian Medical Systems iLab, Baden, Switzerland). For the qualitative analysis, 39 prostate patients are analysed by six clinicians. Clinicians are asked to rate the auto-segmented organs (prostate, bladder, rectum and femoral heads) and to indicate the number of slices to correct. For the quantitative analysis, seven radiation oncologists are asked to contour seven prostate patients. The individual clinician contour variations are compared to the automatic contours by means of surface and volume statistics, calculating the relative volume errors and both the volume and slice-by-slice degree of support, a statistical metric developed for the purposes of this validation. RESULTS: The mean time needed for the automatic module to contour the four structures is about one minute on a standard computer. The qualitative evaluation using a score with four levels ("not acceptable", "acceptable", "good" and "excellent") shows that the mean score for the automatically contoured prostate is "good"; the bladder scores between "excellent" and "good"; the rectum scores between "acceptable" and not acceptable degree of support given to the automatic module is comparable to the relative agreement among the clinicians for prostate and bladder. The slice-by-slice analysis of the surface degree of support pinpointed the areas of disagreement among the clinicians as well as between the clinicians and the automatic module. CONCLUSION: The efficiency and the limits of the automatic module are investigated with both a qualitative and a quantitative analysis. In general, with efficient correction tools at hand, the use of this auto-segmentation module will lead to a time gain for the prostate and the bladder; with the present version of the algorithm, modelling of the rectum still needs improvement. For the quantitative validation, the concept of relative volume error and degree of support proved very useful.
机译:目的:这项工作描述了基于前列腺癌的基于CT的放射治疗计划中自动分割算法的临床验证。材料和方法:经过验证的自动分割算法(智能分割,版本1.0.05)是一种基于规则的算法,使用解剖学参考点和特定于器官的分割方法,由瓦里安医疗系统公司(瓦里安医疗系统iLab,巴登,瑞士)开发)。为了进行定性分析,六名临床医生对39名前列腺患者进行了分析。要求临床医生对自动分割的器官(前列腺,膀胱,直肠和股骨头)进行评分,并指出要纠正的切片数量。为了进行定量分析,要求七名放射肿瘤学家勾勒出七名前列腺患者的轮廓。通过表面和体积统计数据将各个临床医生的轮廓变化与自动轮廓进行比较,计算相对体积误差以及支撑的体积和逐层切片度,这是为此验证而开发的统计度量。结果:在标准计算机上,自动模块绘制四个结构轮廓所需的平均时间约为一分钟。使用具有四个级别(“不可接受”,“可接受”,“良好”和“优秀”)的评分进行定性评估,结果显示自动轮廓化前列腺的平均评分为“良好”;膀胱得分介于“优秀”和“良好”之间;给予自动模块的“可接受的”和不可接受的支撑程度之间的直肠评分可与临床医生对前列腺和膀胱的相对同意相媲美。对支撑物表面程度的逐层分析指出了临床医生之间以及临床医生与自动模块之间的分歧区域。结论:通过定性和定量分析研究了自动模块的效率和限制。通常,在使用有效的校正工具的情况下,使用此自动分段模块将导致前列腺和膀胱的时间增加;使用该算法的当前版本,直肠建模仍需要改进。对于定量验证,相对体积误差和支持度的概念非常有用。

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