Marker-controlled watershed for lymphoma segmentation in sequential CT images

Segmentation of lymphoma containing lymph nodes is a difficult task because of multiple variables associated with the tumor's location, intensity distribution, and contrast to its surrounding tissues. In this paper, we present a reliable and practical marker-controlled watershed algorithm for semi-automated segmentation of lymphoma in sequential CT images. Robust determination of internal and external markers is the key to successful use of the marker-controlled watershed transform in the segmentation of lymphoma and is the focus of this work. The external marker in our algorithm is the circle enclosing the lymphoma in a single slice. The internal marker, however, is determined automatically by combining techniques including Canny edge detection, thresholding, morphological operation, and distance map estimation. To obtain tumor volume, the segmented lymphoma in the current slice needs to be propagated to the adjacent slice to help determine the external and internal markers for delineation of the lymphoma in that slice. The algorithm was applied to 29 lymphomas (size range, 9-53 mm in diameter; mean, 23 mm) in nine patients. A blinded radiologist manually delineated all lymphomas on all slices. The manual result served as the "gold standard" for comparison. Several quantitative methods were applied to objectively evaluate the performance of the segmentation algorithm. The algorithm received a mean overlap, overestimation, and underestimation ratios of 83.2%, 13.5%, and 5.5%, respectively. The mean average boundary distance and Hausdorff boundary distance were 0.7 and 3.7 mm. Preliminary results have shown the potential of this computer algorithm to allow reliable segmentation and quantification of lymphomas on sequential CT images.     

Automatic segmentation of phase-correlated CT scans through nonrigid image registration using geometrically regularized free-form deformation

Conventional radiotherapy is planned using free-breathing computed tomography (CT), ignoring the motion and deformation of the anatomy from respiration. New breath-hold-synchronized, gated, and four-dimensional (4D) CT acquisition strategies are enabling radiotherapy planning utilizing a set of CT scans belonging to different phases of the breathing cycle. Such 4D treatment planning relies on the availability of tumor and organ contours in all phases. The current practice of manual segmentation is impractical for 4D CT, because it is time consuming and tedious. A viable solution is registration-based segmentation, through which contours provided by an expert for a particular phase are propagated to all other phases while accounting for phase-to-phase motion and anatomical deformation. Deformable image registration is central to this task, and a free-form deformation-based nonrigid image registration algorithm will be presented. Compared with the original algorithm, this version uses novel, computationally simpler geometric constraints to preserve the topology of the dense control-point grid used to represent free-form deformation and prevent tissue fold-over. Using mean squared difference as an image similarity criterion, the inhale phase is registered to the exhale phase of lung CT scans of five patients and of characteristically low-contrast abdominal CT scans of four patients. In addition, using expert contours for the inhale phase, the corresponding contours were automatically generated for the exhale phase. The accuracy of the segmentation (and hence deformable image registration) was judged by comparing automatically segmented contours with expert contours traced directly in the exhale phase scan using three metrics: volume overlap index, root mean square distance, and Hausdorff distance. The accuracy of the segmentation (in terms of radial distance mismatch) was approximately 2 mm in the thorax and 3 mm in the abdomen, which compares favorably to the accuracies reported elsewhere. Unlike most prior work, segmentation of the tumor is also presented. The clinical implementation of 4D treatment planning is critically dependent on automatic segmentation, for which is offered one of the most accurate algorithms yet presented.     

Validation of an accelerated 'demons' algorithm for deformable image registration in radiation therapy

A greyscale-based fully automatic deformable image registration algorithm, originally known as the 'demons' algorithm, was implemented for CT image-guided radiotherapy. We accelerated the algorithm by introducing an 'active force' along with an adaptive force strength adjustment during the iterative process. These improvements led to a 40% speed improvement over the original algorithm and a high tolerance of large organ deformations. We used three methods to evaluate the accuracy of the algorithm. First, we created a set of mathematical transformations for a series of patient's CT images. This provides a 'ground truth' solution for quantitatively validating the deformable image registration algorithm. Second, we used a physically deformable pelvic phantom, which can measure deformed objects under different conditions. The results of these two tests allowed us to quantify the accuracy of the deformable registration. Validation results showed that more than 96% of the voxels were within 2 mm of their intended shifts for a prostate and a head-and-neck patient case. The mean errors and standard deviations were 0.5 mm+/-1.5 mm and 0.2 mm+/-0.6 mm, respectively. Using the deformable pelvis phantom, the result showed a tracking accuracy of better than 1.5 mm for 23 seeds implanted in a phantom prostate that was deformed by inflation of a rectal balloon. Third, physician-drawn contours outlining the tumour volumes and certain anatomical structures in the original CT images were deformed along with the CT images acquired during subsequent treatments or during a different respiratory phase for a lung cancer case. Visual inspection of the positions and shapes of these deformed contours agreed well with human judgment. Together, these results suggest that the accelerated demons algorithm has significant potential for delineating and tracking doses in targets and critical structures during CT-guided radiotherapy.     

Registration-based Automatic 3D Segmentation of Cardiac CT Images

Segmenting whole heart from cardiac computed tomography(CT images can provide an important basis for the evaluation of cardiac function and help improve the accuracy of clinical diagnosis. Manual segmentation is the most accurate method for cardiac segmentation. But it is time consuming and not sufficiently reproducible. However, clinicians still rely on this method in practical applications. So a fully automatic method is needed to improve the segmentation efficiency. This pape proposes a registration-based automatic approach for three-dimensional(3D segmentation of cardiac CT images. The proposed method utilizes the similarity o cardiac CT images between different individuals, and uses registration to achieve the segmentation. Affine transformation is firstly implemented to achieve global coarse registration. Then, cubic B-splines are used to refine the local details in locally accurate registration. Mutual information(Ml) is used as the similarity measure, and adaptive stochastic gradient descent(ASGD) as the optimization algorithm. Ou method is applied to the dual-source cardiac CT images to segment whole heart Experimental results show that the proposed method can automatically segment whole heart from cardiac CT images.     

Automatic Delineation of the Diaphragm in Computed Tomographic Images

Segmentation of the internal organs in medical images is a difficult task. By incorporating a priori information regarding specific organs of interest, results of segmentation may be improved. Landmarking (i.e., identifying stable structures to aid in gaining more knowledge concerning contiguous structures) is a promising segmentation method. Specifically, segmentation of the diaphragm may help in limiting the scope of segmentation methods to the abdominal cavity; the diaphragm may also serve as a stable landmark for identifying internal organs, such as the liver, the spleen, and the heart. A method to delineate the diaphragm is proposed in the present work. The method is based upon segmentation of the lungs, identification of the lower surface of the lungs as an initial representation of the diaphragm, and the application of least-squares modeling and deformable contour models to obtain the final segmentation of the diaphragm. The proposed procedure was applied to nine X-ray computed tomographic (CT) exams of four pediatric patients with neuroblastoma. The results were evaluated against the boundaries of the diaphragm as identified independently by a radiologist. Good agreement was observed between the results of segmentation and the reference contours drawn by the radiologist, with an average mean distance to the closest point of 5.85 mm over a total of 73 CT slices including the diaphragm.     

Quantifying the accuracy of automated structure segmentation in 4D CT images using a deformable image registration algorithm

Four-dimensional (4D) radiotherapy is the explicit inclusion of the temporal changes in anatomy during the imaging, planning, and delivery of radiotherapy. One key component of 4D radiotherapy planning is the ability to automatically ("auto") create contours on all of the respiratory phase computed tomography (CT) datasets comprising a 4D CT scan, based on contours manually drawn on one CT image set from one phase. A tool that can be used to automatically propagate manually drawn contours to CT scans of other respiratory phases is deformable image registration. The purpose of the current study was to geometrically quantify the difference between automatically generated contours with manually drawn contours. Four-DCT data sets of 13 patients consisting of ten three-dimensional CT image sets acquired at different respiratory phases were used for this study. Tumor and normal tissue structures [gross tumor volume (GTV), esophagus, right lung, left lung, heart and cord] were manually drawn on each respiratory phase of each patient. Large deformable diffeomorphic image registration was performed to map each CT set from the peak-inhale respiration phase to the CT image sets corresponding with subsequent respiration phases. The calculated displacement vector fields were used to deform contours automatically drawn on the inhale phase to the other respiratory phase CT image sets. The code was interfaced to a treatment planning system to view the resulting images and to obtain the volumetric, displacement, and surface congruence information; 692 automatically generated structures were compared with 692 manually drawn structures. The auto- and manual methods showed similar trends, with a smaller difference observed between the GTVs than other structures. The auto-contoured structures agree with the manually drawn structures, especially in the case of the GTV, to within published interobserver variations. For the GTV, fractional volumes agree to within 0.2+/-0.1, center of mass displacements agree to within 0.5+/-1.5 mm, and agreement of surface congruence is 0.0+/-1.1 mm. The surface congruence between automatic and manual contours for the GTV, heart, left lung, right lung and esophagus was less than 5 mm in 99%, 94%, 94%, 91% and 89%, respectively. Careful assessment of the performance of automatic algorithms is needed in the presence of 4D CT artifacts.     

基于机器学习的鼻咽癌转移淋巴结鉴别模型

目的:研究使用机器学习与影像组学建立用于鼻咽癌CT图像中鉴别转移淋巴结的模型。方法:选择50例鼻咽癌患者初诊CT平扫及静脉灌注增强图像及18F-FGD-PET图像,患者均经病理及PET检查证实为鼻咽癌伴局部淋巴结转移。手动勾画患者CT图像中体积＞1 cm3的淋巴结,由18F-FGD-PET图像中对应区域SUVmax＞2.5及现行影像学标准作为转移与否的分类标准。研究中共获得143枚淋巴结,其中转移淋巴结103枚。使用机器学习方法对上述分类结果进行训练,其中列入训练组淋巴结100枚,验证组43枚,分组方式为随机分组以避免特定的分组方式造成的系统误差。结果:机器学习过程中获得由淋巴结体积、最大横截面短轴及数个影像组学特征构建模型,模型对转移淋巴结的鉴别准确率可达86%。特征选择结果得出:最大横截面直径、平均宽度、灰度强度能量、像素数量、频度、形态密实度等可作为诊断转移淋巴结的重要特征。结论:研究中建立的鉴别模型可在CT图像中实现辅助诊断转移淋巴结,为影像检查中快速判定鼻咽癌患者淋巴结是否转移提供一种新思路,有利于个体化放疗中靶区的精准勾画。     

基于VIP-Man图谱配准的腹部器官分割方法

提出了一种基于图谱配准的腹部器官分割方法.首先将一套预标记图谱向个体图像进行配准,建立二者之间器官的基本对应关系,同时完成对感兴趣器官的识别,其中配准包含全局配准和器官配准.然后,借助已配准的图谱,采用模糊连接方法对感兴趣器官进行分割.腹PCT和MR实验测试结果证明:这种方法实现了模糊连接分割方法中各项参数的自动指定,减轻了人工负担,提高了结果的可靠性.     

Automated matching of temporally sequential CT sections

In the evaluation of patient response to therapy through measurements on thoracic computed tomography (CT) scans, the selection of anatomically equivalent sections in temporally sequential scans is required. We developed an automated method based on normalized mutual information (NMI) to expedite the selection of anatomically equivalent sections. The method requires as input two temporally sequential CT scans from the same patient. A specified section from the baseline scan is then compared with the sections of a follow-up scan. Each section in the follow-up scan is successively translated and rotated relative to the baseline section, and NMI is calculated. The section in the follow-up scan that yields the highest NMI with respect to the baseline section is selected as the matching section. The method was applied to a database of 22 pairs of temporally sequential CT scans from mesothelioma patients. Five observers manually selected their choice of the best anatomically matched section for each of three predetermined sections in the 22 baseline scans, and the range of selected sections was recorded. The automated method was applied to the same baseline sections to determine the computer-based anatomically matched sections in the corresponding follow-up scan. The automated process was performed using both original CT sections and sections automatically segmented so that only intrathoracic pixels contributed to NMI calculations. The accuracy of the automated method was quantified on a section-by-section basis by comparison with the range of sections selected by the observers. The automated method without segmentation selected equivalent sections within the observers' range for 54 of the 66 matching tasks (81.8%). An 11% improvement was achieved when thoracic segmentation was performed as a pre-processing step.     

Pulmonary nodule registration in serial CT scans based on rib anatomy and nodule template matching

An automated method is being developed in order to identify corresponding nodules in serial thoracic CT scans for interval change analysis. The method uses the rib centerlines as the reference for initial nodule registration. A spatially adaptive rib segmentation method first locates the regions where the ribs join the spine, which define the starting locations for rib tracking. Each rib is tracked and locally segmented by expectation-maximization. The ribs are automatically labeled, and the centerlines are estimated using skeletonization. For a given nodule in the source scan, the closest three ribs are identified. A three-dimensional (3D) rigid affine transformation guided by simplex optimization aligns the centerlines of each of the three rib pairs in the source and target CT volumes. Automatically defined control points along the centerlines of the three ribs in the source scan and the registered ribs in the target scan are used to guide an initial registration using a second 3D rigid affine transformation. A search volume of interest (VOI) is then located in the target scan. Nodule candidate locations within the search VOI are identified as regions with high Hessian responses. The initial registration is refined by searching for the maximum cross-correlation between the nodule template from the source scan and the candidate locations. The method was evaluated on 48 CT scans from 20 patients. Experienced radiologists identified 101 pairs of corresponding nodules. Three metrics were used for performance evaluation. The first metric was the Euclidean distance between the nodule centers identified by the radiologist and the computer registration, the second metric was a volume overlap measure between the nodule VOIs identified by the radiologist and the computer registration, and the third metric was the hit rate, which measures the fraction of nodules whose centroid computed by the computer registration in the target scan falls within the VOI identified by the radiologist. The average Euclidean distance error was 2.7 +/- 3.3 mm. Only two pairs had an error larger than 10 mm. The average volume overlap measure was 0.71 +/- 0.24. Eighty-three of the 101 pairs had ratios larger than 0.5, and only two pairs had no overlap. The final hit rate was 93/101.     

How does CT image noise affect 3D deformable image registration for image-guided radiotherapy planning?

PURPOSE: To measure the sensitivity of deformable image registration to image noise. Deformable image registration can be used to map organ contours and other treatment planning data from one CT to another. These CT studies can be acquired with either conventional fan-beam CT systems or more novel cone-beam CT techniques. However, cone-beam CT images can have higher noise levels than fan-beam CT, which might reduce registration accuracy. We have investigated the effect of image quality differences on the deformable registration of fan-beam CTs and CTs with simulated cone-beam noise. METHOD: Our study used three CT studies for each of five prostate patients. Each CT was contoured by three experienced radiation oncologists. For each patient, one CT was designated the source image and the other two were target images. A deformable image registration process was used to register each source CT to each target CT and then transfer the manually drawn treatment planning contours from the source CT to the target CTs. The accuracy of the automatically transferred contours (and thus of the deformable registration process) was assessed by comparing them to the manual contours on the target CTs, with the differences evaluated with respect to interobserver variability in the manual contours. Then each of the target CTs was modified to include increased noise characteristic of cone-beam CT and the tests were repeated. Changes in registration accuracy due to increased noise were detected by monitoring changes in the automatically transferred contours. RESULTS: We found that the additional noise caused no significant loss of registration accuracy at magnitudes that exceeded what would normally be found in an actual cone-beam CT. SUMMARY: We conclude that noise levels in cone-beam CTs that might reduce manual contouring accuracy do not reduce image registration and automatic contouring accuracy.     

淋巴瘤累及胃结肠韧带的CT表现

目的 探讨非霍奇金氏淋巴瘤（non-Hodgkin’s lymphoma,NHL）累及胃结肠韧带（gastrocolic ligament,GCL）的CT表现及其解剖学基础。方法 回顾性分析26例经病理确诊的累及GCL的NHL病例,着重观察GCL的CT表现。结果 GCL受累主要表现为GCL内淋巴结增大,增大淋巴结呈散在分布,未见淋巴结融合。增大淋巴结表现为均匀强化者25例,均匀强化合并环状强化者1例。有2例显示GCL增厚、局部呈饼状改变,伴有腹腔积液。结论 熟悉GCL影像解剖学特点有助于累及GCL的NHL的CT诊断。     

Simulation of four-dimensional CT images from deformable registration between inhale and exhale breath-hold CT scans

PURPOSE: We propose to simulate an artificial four-dimensional (4-D) CT image of the thorax during breathing. It is performed by deformable registration of two CT scans acquired at inhale and exhale breath-hold. MATERIALS AND METHODS: Breath-hold images were acquired with the ABC (Active Breathing Coordinator) system. Dense deformable registrations were performed. The method was a minimization of the sum of squared differences (SSD) using an approximated second-order gradient. Gaussian and linear-elastic vector field regularizations were compared. A new preprocessing step, called a priori lung density modification (APLDM), was proposed to take into account lung density changes due to inspiration. It consisted of modulating the lung densities in one image according to the densities in the other, in order to make them comparable. Simulated 4-D images were then built by vector field interpolation and image resampling of the two initial CT images. A variation in the lung density was taken into account to generate intermediate artificial CT images. The Jacobian of the deformation was used to compute voxel values in Hounsfield units. The accuracy of the deformable registration was assessed by the spatial correspondence of anatomic landmarks located by experts. RESULTS: APLDM produced statistically significantly better results than the reference method (registration without APLDM preprocessing). The mean (and standard deviation) of distances between automatically found landmark positions and landmarks set by experts were 2.7(1.1) mm with APLDM, and 6.3(3.8) mm without. Interexpert variability was 2.3(1.2) mm. The differences between Gaussian and linear elastic regularizations were not statistically significant. In the second experiment using 4-D images, the mean difference between automatic and manual landmark positions for intermediate CT images was 2.6(2.0) mm. CONCLUSION: The generation of 4-D CT images by deformable registration of inhale and exhale CT images is feasible. This can lower the dose needed for 4-D CT acquisitions or can help to correct 4-D acquisition artifacts. The 4-D CT model can be used to propagate contours, to compute a 4-D dose map, or to simulate CT acquisitions with an irregular breathing signal. It could serve as a basis for 4-D radiation therapy planning. Further work is needed to make the simulation more realistic by taking into account hysteresis and more complex voxel trajectories.     

基于人工智能的多模态影像辅助海马体自动勾画研究

目的:利用基于深度学习的人工智能算法,结合头颅MRI和CT的多模态影像,开发海马结构自动勾画技术,为头颅放疗过程中海马体的保护提供高效、准确的自动勾画方法。方法:收集清华大学第一附属医院放疗科从2020年1月~12月就诊的40例脑转移癌患者的定位头颅CT及MRI影像,分别在CT图像、CT-MRI配准图像的两个数据集上训练3D U-Net、3D U-Net Cascade、3D BUC-Net 3个深度学习模型,计算3个模型自动分割的左右海马体与对应的人工标注之间的Dice相似系数（DSC）和95%豪斯多夫距离（95 HD）,以及两者的体积作为模型的分割准确性的评估,并且以对同一大小patch图像的自动分割耗时作为模型效率的评估。结果:引入MRI图像信息对左右海马的自动分割精度有明显的提升;模型3D BUC-Net在CT-MRI数据集上对左右海马体的自动分割都取得最好分割结果（DSC:0.900±0.017,0.882±0.026;95HD:0.792±0.084,0.823±0.093）,而且该模型的分割效率更高。结论:模型3D BUC-Net能在多模态影像上实现高效、准确的海马区的自动勾画,为头颅放疗过程中海马区的保护提供方便。     

Robust and fast shell registration in PET and MR/CT brain images

A robust and fast hybrid method using a shell volume that consists of high contrast voxels with their neighbors is proposed for registering PET and MR/CT brain images. Whereas conventional hybrid methods find the best matched pairs from several manually selected or automatically extracted local regions, our method automatically selects a shell volume in the PET image, and finds the best matched corresponding volume using normalized mutual information (NMI) in overlapping volumes while transforming the shell volume into an MR or CT image. A shell volume not only can reduce irrelevant corresponding voxels between two images during optimization of transformation parameters, but also brings a more robust registration with less computational cost. Experimental results on clinical data sets showed that our method successfully aligned all PET and MR/CT image pairs without losing any diagnostic information, while the conventional registration methods failed in some cases.     

牙颌CT图像序列中牙的半自动分割方法

牙颌CT图像序列相邻切片之间,相应牙的大小、位置以及牙区域和轮廓的灰度分布等特征比较接近,并呈一定的变化规律,根据这一特点提出了牙颌CT图像序列中牙的半自动分割方法。首先选取参考切片,加入少量用户操作进行参考切片中牙轮廓的提取,接着以参考切片为起始切片,由已完成轮廓提取的牙包围盒作为待处理切片(相邻切片)相应牙的操作区间,然后在此区间内用区域生长法提取牙轮廓,由此逐张切片处理可以自动地得到所有切片全牙列每颗牙的轮廓。实验结果表明,本方法仅需少量用户交互就能快速、基本准确地从牙颌CT图像序列中分割出牙轮廓,具有一定的实用价值。     

Automatic re-contouring in 4D radiotherapy

Delineating regions of interest (ROIs) on each phase of four-dimensional (4D) computed tomography (CT) images is an essential step for 4D radiotherapy. The requirement of manual phase-by-phase contouring prohibits the routine use of 4D radiotherapy. This paper develops an automatic re-contouring algorithm that combines techniques of deformable registration and surface construction. ROIs are manually contoured slice-by-slice in the reference phase image. A reference surface is constructed based on these reference contours using a triangulated surface construction technique. The deformable registration technique provides the voxel-to-voxel mapping between the reference phase and the test phase. The vertices of the reference surface are displaced in accordance with the deformation map, resulting in a deformed surface. The new contours are reconstructed by cutting the deformed surface slice-by-slice along the transversal, sagittal or coronal direction. Since both the inputs and outputs of our automatic re-contouring algorithm are contours, it is relatively easy to cope with any treatment planning system. We tested our automatic re-contouring algorithm using a deformable phantom and 4D CT images of six lung cancer patients. The proposed algorithm is validated by visual inspections and quantitative comparisons of the automatic re-contours with both the gold standard segmentations and the manual contours. Based on the automatic delineated ROIs, changes of tumour and sensitive structures during respiration are quantitatively analysed. This algorithm could also be used to re-contour daily images for treatment evaluation and adaptive radiotherapy.     

腹腔淋巴结影像断层解剖学的研究

目的为腹腔淋巴结的临床诊断提供断层影像解剖学资料。方法对6具尸体标本的上腹部横断层、CT、MRI扫描图像进行观察,观察腹腔淋巴结的位置,测量其横断面矢径和横径,辨认在MRI、CT扫描图像中的腹腔淋巴结。结果共观察到8个腹腔淋巴结,单具标本腹腔淋巴结的数目有1～2个;其横断面呈椭圆形或长椭圆形,矢径为（5.27±2.18）mm,横径为（2.35±1.12）mm,组织疏松较软,横断面上可见管腔断面;在MRI扫描图像上能被清晰分辨,在尸体标本的CT扫描图像上与周围结构界线不清。结论腹腔淋巴结与腹腔神经节的鉴别,在断面标本中可通过观察组织是否致密,有无管腔断面进行确定;在CT、MRI扫描图像上主要根据他们的位置关系进行确定。     

启发式牙颌CT影像自动分割

准确快速地分割CT切片特征轮廓是医学图像三维重建的重要环节。现有的轮廓分割方法必须通过手动层层交互操作,不仅耗时而且分割精度不高。针对这种局限性,提出一种基于启发式牙颌CT影像自动分割方法。首先用拉普拉斯算子对CT图像序列进行边缘增强,其次用轮廓匹配映射技术实现轮廓启发式传递,最后基于收缩包围算法自动分割牙颌序列。以14例完整牙（每例28～32颗牙数据样本）锥束CT断层扫描图像序列进行实验,在相同条件下分别用所提出的轮廓自动提取方法和其他提取方法,对实验样本进行轮廓提取,得到单颗牙轮廓提取的平均用时和提取轮廓与真实轮廓之间的距离差平均值。实验结果显示,轮廓自动分割算法提取单颗牙轮廓的用时约为其他手工分割法提取单颗牙轮廓用时的23%,同时提取的轮廓质量和用传统方法提取的轮廓质量相当。该方法为CT数据特征区自动化分割提供一种可行且高效的方法,为进一步改进现有的CT影像分割和三维重建算法提供了新的思路。     

Quantification of accuracy of the automated nonlinear image matching and anatomical labeling (ANIMAL) nonlinear registration algorithm for 4D CT images of lung

The performance of the ANIMAL (Automated Nonlinear Image Matching and Anatomical Labeling) nonlinear registration algorithm for registration of thoracic 4D CT images was investigated. The algorithm was modified to minimize the incidence of deformation vector discontinuities that occur during the registration of lung images. Registrations were performed between the inhale and exhale phases for five patients. The registration accuracy was quantified by the cross-correlation of transformed and target images and distance to agreement (DTA) measured based on anatomical landmarks and triangulated surfaces constructed from manual contours. On average, the vector DTA between transformed and target landmarks was 1.6 mm. Comparing transformed and target 3D triangulated surfaces derived from planning contours, the average target volume (GTV) center-of-mass shift was 2.0 mm and the 3D DTA was 1.6 mm. An average DTA of 1.8 mm was obtained for all planning structures. All DTA metrics were comparable to inter observer uncertainties established for landmark identification and manual contouring.