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首页> 外文会议>Image Processing pt.3; Progress in Biomedical Optics and Imaging; vol.8,no.31; Proceedings of SPIE-The International Society for Optical Engineering; vol.6512 pt.3 >Comparative evaluation of a novel 3D segmentation algorithm on in- treatment radiotherapy cone beam CT images
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Comparative evaluation of a novel 3D segmentation algorithm on in- treatment radiotherapy cone beam CT images

机译：新型3D分割算法在治疗性放射治疗锥束CT图像中的比较评估

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摘要

Image segmentation and delineation is at the heart of modern radiotherapy, where the aim is to deliver as high a radiation dose as possible to a cancerous target whilst sparing the surrounding healthy tissues. This, of course, requires that a radiation oncologist dictates both where the tumour and any nearby critical organs are located. As well as in treatment planning, delineation is of vital importance in image guided radiotherapy (IGRT): organ motion studies demand that features across image databases are accurately segmented, whilst if on-line adaptive IGRT is to become a reality, speedy and correct target identification is a necessity. Recently, much work has been put into the development of automatic and semi-automatic segmentation tools, often using prior knowledge to constrain some grey level, or derivative thereof, interrogation algorithm. It is hoped that such techniques can be applied to organ at risk and rumour segmentation in radiotherapy. In this work, however, we make the assumption that grey levels do not necessarily determine a tumour's extent, especially in CT where the attenuation coefficient can often vary little between cancerous and normal tissue. In this context we present an algorithm that generates a discontinuity free delineation surface driven by user placed, evidence based support points. In regions of sparse user supplied information, prior knowledge, in the form of a statistical shape model, provides guidance. A small case study is used to illustrate the method. Multiple observers (between 3 and 7) used both the presented tool and a commercial manual contouring package to delineate the bladder on a serially imaged (10 cone beam CT volumes ) prostate patient. A previously presented shape analysis technique is used to quantitatively compare the observer variability.
机译：图像分割和轮廓描绘是现代放射疗法的核心，其目的是在保留周围健康组织的同时，向癌性靶标提供尽可能高的放射剂量。当然，这需要放射肿瘤学家指示肿瘤和附近任何重要器官的位置。在治疗计划中，划定在影像引导放射治疗（IGRT）中至关重要：器官运动研究要求准确分割影像数据库中的特征，而如果要使在线自适应IGRT成为现实，迅速且正确的目标身份识别是必要的。近来，在自动和半自动分割工具的开发中投入了大量工作，经常使用先验知识来约束某些灰度级或其衍生的询问算法。希望可以将这种技术应用于放射治疗中有风险的器官和谣言分割。但是，在这项工作中，我们假设灰度级不一定确定肿瘤的程度，特别是在CT中，衰减系数通常在癌组织和正常组织之间变化很小。在这种情况下，我们提出一种算法，该算法生成由用户放置的，基于证据的支持点驱动的不间断的自由轮廓表面。在用户提供的信息稀少的区域中，以统计形状模型的形式提供的先验知识可以提供指导。一个小案例研究用于说明该方法。多个观察员（3到7岁之间）使用所提供的工具和商业手动轮廓包来描绘出连续成像（10个锥形束CT体积）前列腺患者的膀胱。先前介绍的形状分析技术用于定量比较观察者的变异性。

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