Automated Object Contouring Methods & Software for Radiotherapy Planning

摘要 2015年，美国估计将新增1,658,370例癌症病例，其中近三分之二将受到辐射 治疗(RT)。鉴于美国有2300多个RT中心，以及现有的器官轮廓系统 在风险(桨)主要依靠手工方法，有一个强大的商业机会生产的软件 一种可在医学图像中高度自动化并用于冲击电流的桨轮廓系统 RT规划的实践。受到我们在胸部和头颈部(H&N)身体的强劲I期结果的鼓舞 地区与当前的行业系统相比，我们寻求准确性、效率和临床接受度 我们的软件产品输出的等高线大大超过现有系统。我们的总体目标是 第二阶段是将第一阶段开发的算法和原型软件推进到领先的商业 软件产品，并在全国多个医疗中心展示其功效 人口。第二阶段的具体目标有三个：(1)进一步推进自动解剖识别 第一阶段的算法使用先进的深度学习技术。(2)开发基于云的软件汽车 轮廓绘制服务。(3)对H&N和胸部病例进行临床评价。 目标1将分三个阶段实现：(A)自动定义给定的身体区域 患者CT研究，目前在我们的系统中手动完成，通过使用虚拟地标的新概念 深度学习技术。(B)从目前的2个体素提高物体识别/定位精度 通过使用虚拟数据，将质量较好的数据集设置为1个体素，并将质量较差的数据集的4-5个体素设置为2-3个体素 学习对象关系的地标。(C)结合目标定位方法改进目标划定 将深度学习技术应用于局部目标附近，以带来边界距离精度 在1个体素内。目标2将通过开发基于云的软件即服务模型来实现 整合了这些算法的软件。为了实现目标3，一项涉及四位学者的评估研究 RT中心将负责评估由以下机构输出的等高线的效率、准确性和可接受性 新软件。为了评估效率，当前临床过程所用的等高线时间将与 新软件方法所需的时间加上所需的任何手动调整。准确性将通过以下方式进行评估 将软件输出与精心准备的地面真实等高线进行比较。可接受性由以下因素决定 进行盲法读者研究，其中放射肿瘤学家给软件的可接受性评分(1-5) 产生等高线、地面真实等高线和正常临床过程产生的等高线，并比较 这些分数。 与以下各项相比，预期的临床结果显著提高了临床效率/轮廓的可接受性 目前的做法。

Abstract In 2015, 1,658,370 new cancer cases are estimated to occur in the US, where nearly two-thirds will have radiation therapy (RT). Given that there are over 2,300 RT centers in the US, and current systems for contouring organs at risk (OARs) rely mostly on manual methods, there is a strong commercial opportunity for producing a software system that can contour OARs in medical images at a high degree of automation and for impacting current practice of RT planning. Encouraged by our strong Phase I results in thoracic and head and neck (H&N) body regions compared to current industry systems, we seek the accuracy, efficiency, and clinical acceptance of the contours output by our software product to significantly exceed those of existing systems. Our overall aim for Phase II is to advance the algorithms and prototype software developed in Phase I into a leading commercial software product, and demonstrate its efficacy in multiple medical centers across the country with diverse populations. Phase II specific aims are three-fold: (1) Further advance the automatic anatomy recognition algorithms from Phase I using advanced deep learning techniques. (2) Develop a cloud-based software auto contouring service. (3) Perform clinical evaluation of the new software on H&N and thoracic cases. Aim 1 will be accomplished in three stages: (a) Automating the process of defining the body region on given patient CT studies, which is currently done manually in our system, via a new concept of virtual landmarks using deep learning techniques. (b) Improving object recognition/ localization accuracy from the current 2 voxels for “good” quality data sets to 1 voxel and from 4-5 voxels for “poor” quality data sets to 2-3 voxels by using virtual landmarks to learn object relationships. (c) Improving object delineation by combining object localization methods with deep learning techniques applied to the vicinity of the localized objects to bring boundary distance accuracy within 1 voxel. Aim 2 will be achieved by developing a cloud-based Software-as-a-Service model to implement the software that incorporates the algorithms. To accomplish Aim 3, an evaluation study involving four academic RT centers will be undertaken to assess the efficiency, accuracy, and acceptability of the contours output by the new software. To assess efficiency, contouring time taken by the current clinical process will be compared to the time taken by the new software method plus any manual adjustment needed. Accuracy will be assessed by comparing software output to carefully prepared ground truth contours. Acceptability will be determined by conducting a blinded reader study, where an acceptability score (1-5) is given by radiation oncologists to software produced contours, ground truth contours, and contours produced by the normal clinical process, and comparing these scores. Expected clinical outcomes are significantly improved clinical efficiency/ acceptability of contouring compared to current practice.