Automatic segmentation of kidneys in computed tomography images using U-Net

PurposeAccurate segmentation of target volumes and organs at risk from computed tomography (CT) images is essential for treatment planning in radiation therapy. The segmentation task is often done manually making it time-consuming. Besides, it is biased to the clinician experience and subject to inter-observer variability. Therefore, and due to the development of artificial intelligence tools and particularly deep learning (DL) algorithms, automatic segmentation has been proposed as an alternative. The purpose of this work is to use a DL-based method to segment the kidneys on CT images for radiotherapy treatment planning.Materials and methodsIn this contribution, we used the CT scans of 20 patients. Segmentation of the kidneys was performed using the U-Net model. The Dice similarity coefficient (DSC), the Matthews correlation coefficient (MCC), the Hausdorff distance (HD), the sensitivity and the specificity were used to quantitatively evaluate this delineation.ResultsThis model was able to segment the organs with a good accuracy. The obtained values of the used metrics for the kidneys segmentation, were presented. Our results were also compared to those obtained recently by other authors.ConclusionFully automated DL-based segmentation of CT images has the potential to improve both the speed and the accuracy of radiotherapy organs contouring.     

基于深度学习自动分割模型乳腺癌放疗临床应用与评价

目的 将深度学习算法与商用计划系统整合,建立乳腺癌靶区和危及器官(OARs)自动分割平台并加以验证。方法 入组在中国医学科学院肿瘤医院行保乳术后放疗的左、右乳腺癌患者各400例。基于深度残差卷积神经网络进行训练临床靶区(CTV)和OARs分割模型,建立端到端的基于深度学习的自动分割平台(DLAS)。使用42例左乳腺癌和40例右乳腺癌验证DLAS平台勾画的准确性。分别计算总体戴斯相似性系数(DSC)和平均豪斯多夫距离(AHD)。并计算相对层位置与每层DSC值(DSC_s)的关系,进行逐层分析。结果 左/右乳腺癌全乳CTV平均总体DSC和AHD分别为0.87/0.88和9.38/8.71mm,左/右乳腺癌OARs平均总体DSC和AHD范围为0.86~0.97和0.89~9.38mm。对CTV和OARs进行逐层分析,达到0.90以上表示医生只需要较少修改甚至不用修改的层面,左右乳腺癌的CTV勾画占比约44.7%的层面,OARs自动勾画占比范围为50.9%~89.6%。对于DSC_s<0.7,在两侧边界区域(层位置0~0.2和0.8~1.0) CTV和除脊髓以外的感兴趣区域DSC_s值明显下降,且越靠近边缘降低程度越明显。脊髓采用全层勾画,未发现有特殊区域出现DSC_s明显下降。结论 建立端到端的DLAS平台整合乳腺癌分割模型取得较好的自动分割效果。在头脚方向的两侧边界区域,勾画的一致性下降较明显,有待进一步提高。     

基于深度学习自动分割模型乳腺癌放疗临床应用与评价

目的 将深度学习算法与商用计划系统整合,建立乳腺癌靶区和危及器官(OARs)自动分割平台并加以验证。方法 入组在中国医学科学院肿瘤医院行保乳术后放疗的左、右乳腺癌患者各400例。基于深度残差卷积神经网络进行训练临床靶区(CTV)和OARs分割模型,建立端到端的基于深度学习的自动分割平台(DLAS)。使用42例左乳腺癌和40例右乳腺癌验证DLAS平台勾画的准确性。分别计算总体戴斯相似性系数(DSC)和平均豪斯多夫距离(AHD)。并计算相对层位置与每层DSC值(DSC_s)的关系,进行逐层分析。结果 左/右乳腺癌全乳CTV平均总体DSC和AHD分别为0.87/0.88和9.38/8.71mm,左/右乳腺癌OARs平均总体DSC和AHD范围为0.86~0.97和0.89~9.38mm。对CTV和OARs进行逐层分析,达到0.90以上表示医生只需要较少修改甚至不用修改的层面,左右乳腺癌的CTV勾画占比约44.7%的层面,OARs自动勾画占比范围为50.9%~89.6%。对于DSC_s<0.7,在两侧边界区域(层位置0~0.2和0.8~1.0) CTV和除脊髓以外的感兴趣区域DSC_s值明显下降,且越靠近边缘降低程度越明显。脊髓采用全层勾画,未发现有特殊区域出现DSC_s明显下降。结论 建立端到端的DLAS平台整合乳腺癌分割模型取得较好的自动分割效果。在头脚方向的两侧边界区域,勾画的一致性下降较明显,有待进一步提高。     

Can Generative Adversarial Networks help to overcome the limited data problem in segmentation?

PurposeFor image translational tasks, the application of deep learning methods showed that Generative Adversarial Network (GAN) architectures outperform the traditional U-Net networks, when using the same training data size. This study investigates whether this performance boost can also be expected for segmentation tasks with small training dataset size.Materials/MethodsTwo models were trained on varying training dataset sizes ranging from 1—100 patients: a) U-Net and b) U-Net with patch discriminator (conditional GAN). The performance of both models to segment the male pelvis on CT-data was evaluated (Dice similarity coefficient, Hausdorff) with respect to training data size.ResultsNo significant differences were observed between the U-Net and cGAN when the models were trained with the same training sizes up to 100 patients. The training dataset size had a significant impact on the models’ performances, with vast improvements when increasing dataset sizes from 1 to 20 patients.ConclusionWhen introducing GANs for the segmentation task no significant performance boost was observed in our experiments, even in segmentation models developed on small datasets.     

宫颈癌自适应放疗中基于自配准与图谱库的自动勾画比较

目的 分析MIM软件在宫颈癌自适应放疗中基于自配准与图谱库的自动勾画的可行性与准确性。方法 选取60例宫颈癌患者的CT图像及勾画结果建立Atlas模板库。随机选取15例宫颈癌患者初次计划CT (pCT)与重新计划CT (rCT),由资深临床医师勾画CTV和危及器官。分别以刚性和形变两种配准方式将pCT的勾画传送到rCT图像上;并对各rCT图像行基于Atlas模板库的自动勾画,统计3种方法所需时间。利用Dice相似性系数(DSC)、交叉指数(OI)、Hausdorff平均距离(AHD)、质心距离(DC)评价勾画结果,并进行单因素方差分析。结果 Atlas组、刚性组和形变组完成1例所需平均时间分别为89.2、22.4、42.6 s。对于CTV和直肠的DSC、OI和AHD,刚性组和形变组与Atlas组之间不同(P<0.001),小肠的OI在刚性组和形变组与Atlas组之间有不同, CTV的DSC平均值分别为0.89(刚性组和形变组)、0.76(Atlas组)。对于膀胱、盆骨和股骨头,形变组的勾画结果最优。结论 形变组的勾画结果优于刚性组和Atlas组,3种勾画方式均能快速地完成靶区和危及器官的自动勾画。     

基于深度学习的宫颈癌放疗靶区及危及器官自动勾画研究

目的:构建基于深度学习（deep learning，DL）的卷积神经网络模型，实现宫颈癌患者放射治疗计划的临床靶区体积（clinical target volume，CTV）和危及器官（organ at risks，OARs）自动勾画。方法:回顾性分析在福建省肿瘤医院行放射治疗的宫颈癌患者99例。对患者CT图像进行预处理，作为模型输入。设计一种基于DL的自动勾画模型，使用组合损失函数训练该模型。以医师手动勾画为度量标准，计算DL自动勾画模型下CTV靶区和膀胱、直肠、乙状结肠、左右骨髓、左右股骨头的准确率，并与基于图谱的自动勾画方法（atlas-based auto segmentation，ABAS）相比较。结果:DL模型在CTV靶区和7种危及器官（膀胱、直肠、乙状结肠、左右骨髓、左右股骨头）的戴斯系数分别为（0.85±0.02、0.94±0.04、0.87±0.03、0.67±0.14、0.85±0.03、0.87±0.03、0.87±0.06和0.87±0.06），95%豪斯多夫距离(mm)分别为（3.22±0.56、1.37±0.37、1.41±0.34、27.39±35.63、1.40±0.17、1.36±0.22、6.78±7.89和6.45±7.44），平均表面距离(mm)分别为（0.25±0.05、0.12±0.06、0.19±0.05、2.29±2.71、0.16±0.04、0.15±0.03、0.36±0.33和0.38±0.37）。DL勾画模型的戴斯系数均高于ABAS勾画模型。除乙状结肠外，DL勾画模型的95%豪斯多夫距离和平均表面距离均小于ABAS勾画模型。结论:提出的DL模型能较好地实现宫颈癌放疗临床靶区和危及器官的自动勾画，可为临床医师勾画提供初步参考，节省临床靶区和危及器官勾画的时间。     

Auto-segmentation of low-risk clinical target volume for head and neck radiation therapy

PurposeTo investigate atlas-based auto-segmentation methods to improve the quality of the delineation of low-risk clinical target volumes (CTVs) of unilateral tonsil cancers.Method and MaterialsSixteen patients received intensity modulated radiation therapy for left tonsil tumors. These patients were treated by a total of 8 oncologists, who delineated all contours manually on the planning CT image. We chose 6 of the patients as atlas cases and used atlas-based auto-segmentation to map each the atlas CTV to the other 10 patients (test patients). For each test patient, the final contour was produced by combining the 6 individual segmentations from the atlases using the simultaneous truth and performance level estimation algorithm. In addition, for each test patient, we identified a single atlas that produced deformed contours best matching the physician's manual contours. The auto-segmented contours were compared with the physician's manual contours using the slice-wise Hausdorff distance (HD), the slice-wise Dice similarity coefficient (DSC), and a total volume overlap index.ResultsNo single atlas consistently produced good results for all 10 test cases. The multiatlas segmentation achieved a good agreement between auto-segmented contours and manual contours, with a median slice-wise HD of 7.4 ± 1.0 mm, median slice-wise DSC of 80.2% ± 5.9%, and total volume overlap of 77.8% ± 3.3% over the 10 test cases. For radiation oncologists who contoured both the test case and one of the atlas cases, the best atlas for a test case had almost always been contoured by the oncologist who had contoured that test case, indicating that individual physician's practice dominated in target delineation and was an important factor in optimal atlas selection.ConclusionsMultiatlas segmentation may improve the quality of CTV delineation in clinical practice for unilateral tonsil cancers. We also showed that individual physician's practice was an important factor in selecting the optimal atlas for atlas-based auto-segmentation.     

自动勾画软件ABAS在鼻咽癌自适应放疗中的应用

目的:评估ABAS自动勾画软件勾画的危及器官准确度和效率,以此来评估它在鼻咽癌患者自适应放疗中的适用程度.方法:随机抽取15例在我院治疗的鼻咽癌患者.CT1为患者的计划CT,CT2为三分之二疗程重薪扫描的CT图像,CT3为患者放疗结束后扫描的CT图像.在ABAS软件中CT1图像设为模板,在CT2和CT3上自动勾画出所需的危及器官,并将自动勾画结果和手工勾画的结果进行对比分析.利用形状相似性指数(Dice similarity coefficient,DSC)和自动勾画时间评价软件自动勾画的精准性和效率性.结果:ABAS软件自动勾画的体积较大的危及器官的DSC指数均大于0.9,在CT1和CT2组中DSC指数的最高为脊髓(0.96±0.01),最低为晶体(0.43±0.19),在CT1和CT3组中DSC指数最高为下颌骨(0.93±0.45),最低为晶体(0.49 ±0.17).同时用ABAS自动勾画危及器官所需平均时间为十分钟左右.结论:在鼻咽癌自适应放疗过程中,自动勾画软件勾画的危器官可以达到很好的准确度同时又明显的节省时间.这样就可以快速评价危及器官受量,使得鼻咽癌自适应放疗成为可能.     

Research on automatic segmentation of female bowel based on Dense V-Network neural network

Objective To resolve the issue of poor automatic segmentation of the bowel in women with pelvic tumors, a Dense V-Network model was established, trained and evaluated to accurately and automatically delineate the bowel of female patients with pelvic tumors. Methods Dense Net and V-Net network models were combined to develop a Dense V-Network algorithm for automatic segmentation of 3D CT images. CT data were collected from 160 patients with cervical cancer, 130 of which were randomly selected as the training set to adjust the model parameters, and the remaining 30 were used as test set to evaluate the effect of automatic segmentation. Results Eight parameters including Dice similarity coefficient (DSC) were utilized to quantitatively evaluate the segmentation effect. The DSC value, JD,ΔV, SI, IncI, HD (cm), MDA (mm), and DC (mm) of the small intestine were 0.86±0.03,0.25±0.04,0.10±0.07,0.88±0.05,0.85±0.05,2.98±0.61,2.40±0.45 and 4.13±1.74, which were better than those of any other single algorithm. Conclusion Dense V-Network algorithm proposed in this paper can deliver accurate segmentation of the bowel organs. It can be applied in clinical practice after slight revision by physicians.     

基于Dense V-Network神经网络的女性肠道自动分割研究

目的 用精准自动勾画女性肠道器官的Dense V-Network模型对宫颈癌患者进行训练并评估。方法 将Dense Net与V-Net2个网络模型进行融合,形成一种用于三维CT图像自动分割的Dense V-Network算法。160例宫颈癌患者CT数据被随机分为训练集130例用于调整模型参数,测试集30例用于评估自动分割效果。采用戴斯相似性系数(DSC)等8个参数定量评估分割效果。结果 小肠DSC、杰卡德距离、体积差异性系数、敏感性指数、包容性指数、豪斯多夫距离、轮廓平均差异、质心偏差分别为0.86±0.03、0.25±0.04、0.10±0.07、0.88±0.05、0.85±0.05、(2.98±0.61) cm、(2.40±0.45) mm、(4.13±1.74) mm,结果优于单一算法(均P<0.05)。结论 Dense V-Network算法可较为准确地分割肠道器官,医生修改审查简单易行,可用于临床。     

自动分割技术在鼻咽癌靶区及危及器官勾画应用价值的研究

目的 基于深度反卷积神经网络(DDNN)自动分割技术,探讨其在鼻咽癌靶区和危及器官(OAR)辅助人工勾画的应用价值。方法 利用已完成治疗的 800例鼻咽癌患者的CT信息,构建基于DDNN算法的端到端自动分割模型,选取 10例新的鼻咽癌患者作为研究测试集。通过比较10名初级医师在自动勾画基础上辅助人工勾画(DLAC)与单纯人工勾画(MC)的精确度系数(DICE)、平均一致距离(MDTA)、变异系数(CV)、标准距离偏差(SDD)、勾画时间等参数以评估自动勾画的效果。结果 在DLAC组,GTV、CTV的DICE分别为 0.67±0.15、0.841±0.032,MDTA分别为(0.315±0.23)、(0.032±0.098)mm,显著优于MC组(P<0.001)。除脊髓、左右晶体、下颌骨外,DLAC组其他OAR的DICE高于MC组,其中下颌骨最高,视交叉最低。此外,相较MC组,DLAC组GTV、CTV、OAR的CV、SDD均显著降低(P<0.001),总勾画时间节省63.7%(P<0.001)。结论 与MC相比,基于DDNN建立的DLAC能更为准确地实现鼻咽癌GTV、CTV和OAR的勾画,可大幅提高医师工作效率及勾画一致性。     

基于混合注意力U-net全脑全脊髓临床靶区自动勾画

目的:在U-net卷积神经网络基础上设计出混合注意力U-net (HA-U-net)网络用于全脑全脊髓临床靶体积(CTV)自动勾画,并与U-net自动分割模型分割结果进行比较。方法:研究回顾了110例全脑全脊髓患者数据,选择80例用于训练集,10例用于验证集,20例作为测试集。HA-U-net以U-net为基准网络,在...     

Synthetic Computed Tomography Generation from 0.35T Magnetic Resonance Images for Magnetic Resonance–Only Radiation Therapy Planning Using Perceptual Loss Models

PurposeMagnetic resonance imaging (MRI) provides excellent soft-tissue contrast, which makes it useful for delineating tumor and normal structures in radiation therapy planning, but MRI cannot readily provide electron density for dose calculation. Computed tomography (CT) is used but introduces registration uncertainty between MRI and CT. Previous studies have shown that synthetic CTs (sCTs) can be generated directly from MRI images with deep learning methods. However, mainly high-field MRI images have been validated. This study tested whether acceptable sCTs for MR-only radiation therapy planning can be synthesized using an integrated MR-guided linear accelerator at 0.35T, using MRI images and treatment plans in the liver region.Methods and MaterialsTwo models were investigated in this study: a convolutional neural network (Unet) with conventional mean square error (MSE) loss and a Unet using a secondary convolutional neural network for perceptual loss. A total of 37 cases were used in this study with 10-fold cross validation, and 37 treatment plans were generated and evaluated for target coverage and dose to organs at risk (OARs) in the MSE loss model, perceptual loss model, and original CT.ResultsThe sCTs predicted by the perceptual loss model had improved subjective visual quality compared with those predicted by the MSE loss model, but both were similar in mean absolute error (MAE), peak-signal-to-noise ratio (PSNR), and normalized cross-correlation (NCC). The MAE, PSNR, and NCC for the perceptual loss model were 35.64, 24.11, and 0.9539, respectively, and those for the MSE loss model were 35.67, 24.36, and 0.9566, respectively. No significant differences in target coverage and dose to OARs were found between the sCT predicted by the perceptual loss model or by the MSE model and the original CT image.ConclusionsThis study indicated that a Unet with both MSE loss and perceptual loss models can be used for generating sCT images from a 0.35T integrated MR linear accelerator.     

基于深度学习多模态风格迁移技术CBCT图像肺癌靶区自动分割方法研究北大核心CSCD

目的针对CBCT图像中肿瘤与周围组织对比度低的缺点, 研究一种适合于CBCT图像中中心型肺癌的自动分割方法。方法收集221例中心型肺癌患者, 其中176例行CT定位, 45例行强化CT定位。将强化CT图像分别设置为肺窗和纵隔窗, 并与首次CBCT验证图像进行弹性配准获得配对数据集;然后将配对数据集传入cycleGAN网络进行风格迁移, 使得CBCT图像可分别转化为肺窗和纵隔窗下的"强化CT";最后经风格迁移后的图像被载入UNET-attention网络对大体肿瘤体积进行深度学习。通过戴斯相似性系数(DSC)、豪斯多夫距离(HD)和受试者工作特征曲线下面积(AUC)对分割结果进行评价。结果经风格迁移后肿瘤与周围组织对比度明显增强, 采用cycleGAN+UNET-attention网络的DSC值为0.78±0.05, HD值为9.22±3.42, AUC值为0.864。结论采用cycleGAN+UNET-attention网络可有效对CBCT图像中中心型肺癌进行自动分割。     

Lung Cancer and Granuloma Identification Using a Deep Learning Model to Extract 3-Dimensional Radiomics Features in CT Imaging

BackgroundWe aimed to evaluate a deep learning (DL) model combining perinodular and intranodular radiomics features and clinical features for preoperative differentiation of solitary granuloma nodules (GNs) from solid lung cancer nodules in patients with spiculation, lobulation, or pleural indentation on CT.Patients and MethodsWe retrospectively recruited 915 patients with solitary solid pulmonary nodules and suspicious signs of malignancy. Data including clinical characteristics and subjective CT findings were obtained. A 3-dimensional U-Net-based DL model was used for tumor segmentation and extraction of 3-dimensional radiomics features. We used the Maximum Relevance and Minimum Redundancy (mRMR) algorithm and the eXtreme Gradient Boosting (XGBoost) algorithm to select the intranodular, perinodular, and gross nodular radiomics features. We propose a medical image DL (IDL) model, a clinical image DL (CIDL) model, a radiomics DL (RDL) model, and a clinical image radiomics DL (CIRDL) model to preoperatively differentiate GNs from solid lung cancer. Five-fold cross-validation was used to select and evaluate the models. The prediction performance of the models was evaluated using receiver operating characteristic and calibration curves.ResultsThe CIRDL model achieved the best performance in differentiating between GNs and solid lung cancer (area under the curve [AUC] = 0.9069), which was significantly higher compared with the IDL (AUC = 0.8322), CIDL (AUC = 0.8652), intra-RDL (AUC = 0.8583), peri-RDL (AUC = 0.8259), and gross-RDL (AUC = 0.8705) models.ConclusionThe proposed CIRDL model is a noninvasive diagnostic tool to differentiate between granuloma nodules and solid lung cancer nodules and reduce the need for invasive diagnostic and surgical procedures.     

基于深度学习的男性盆腔CT影像自动分割概述

近年来,深度学习已逐渐应用于放射治疗的器官自动分割和勾画。但是基于CT图像的盆腔器官自动分割仍具有较大挑战性。本文介绍了图像分割常用的基础网络模型和框架,以及适用于医学图像分割的网络、损失函数、常用数据集改进,重点概述了近五年基于CT图像使用深度学习自动分割男性盆腔器官的主要网络和结果,探讨了深度学习自动分割所面临的挑战和局限性,以及未来潜在的研究方向。     

ABAS软件勾画OAR临床前测试重要性研究

目的 对基于模板自动分区(ABAS)算法的图像勾画软件进行临床前测试,评估鼻咽癌放疗计划OAR勾画精度,为确定临床应用条件提供依据。方法 以放疗医师在22例鼻咽癌患者放疗计划CT图像上手工勾画的OAR结构为评价标准,分别对ABAS软件两种算法(General和Head/Neck)自动勾画的OAR进行以下测试:(1)每1例患者均拷贝1套图像,以原图像上手工勾画的轮廓为模板在拷贝图像上自动勾画,考察自动勾画对模板的还原能力;(2)以1例患者图像上手工勾画的轮廓为模板,对其余患者图像进行自动勾画,考察采用单一模板对不同患者图像自动勾画的准确度。评价指标包括各OAR的DSC、V_diff、DSC与勾画体积相关性,以及自动勾画加手工修改与单纯手工勾画的耗时差别。Wilcoxon符号秩检验,Spearman相关性分析。结果 Head/Neck算法对模板还原能力优于或相当于General算法,自动勾画DSC与所勾画结构体积大小呈正相关(r_s=0.879、0.939)。还原测试中体积>1 cm³器官自动勾画的DSC>0.8。使用Head/Neck算法基于单一模板的自动勾画中,脑干、颞叶、腮腺、下颌骨的DSC和V_diff平均值分别为0.81~0.90和2.73%~16.02%,颞颌关节和视交叉DSC为0.45~0.49。应用自动勾画加手工修改比单纯手工勾画可以节省68%时间。结论 临床前测试可以确定ABAS算法在特定临床应用条件的准确度和适用范围,所测试软件可帮助提高鼻咽癌放疗计划OAR勾画效率,但不适用于较小体积器官的勾画。     

Clinical evaluation of deep learning-based clinical target volume auto-segmentation algorithm for cervical cancer

Objective To validate the feasibility of a deep learning-based clinical target volume (CTV) auto-segmentation algorithm for cervical cancer in clinical settings. Methods CT data sets from 535 cervical cancer patients were collected. CTVs were delineated according to RTOG and JCOG guidelines, reviewed by experts, and then used as reference contours for training (definitive 177, post-operative 302) and test (definitive 23, post-operative 33). Four definitive and 6 post-operative cases were randomly selected from the testing cohort to be manually delineated by junior, intermediate, senior doctors, respectively. Dice coefficient (DSC), mean surface distance (MSD) and Hausdorff distance (HD) were used for test and comparison between auto-segmentation and RO delineation. Meantime, auto-segmentation time and manual delineation time were recorded. Results Auto-segmentation models of dCTV1, dCTV2 and pCTV1 were trained with VB-Net and showed good agreement with reference contours in the testing cohorts (DSC, 0.88,0.70,0.86 mm;MSD,1.32,2.42,1.15 mm;HD,21.6,22.4,20.8 mm). For dCTV1, the difference between auto-segmentation and all three groups of doctors was not significant (P>0.05). For dCTV2 and pCTV1, auto-segmentation was better than the junior and intermediate doctors (both P<0.05). Auto-segmentation time consumption was considerably shorter than that of manual delineation. Conclusions Deep learning-based CTV auto-segmentation algorithm for cervical cancer achieves comparable accuracy to manual delineation of senior doctors. Clinical application of the algorithm can contribute to shortening doctors′ manual delineation time and improving clinical efficiency. Furthermore, it may serve as a guide for junior doctors to improve the consistency and accuracy of cervical cancer CTV delineation in clinical practice.     

Application of aSi-kVCBCT for Volume Assessment and Dose Estimation: An Offline Adaptive Study for Prostate Radiotherapy

Objective: The purpose of this study is to develop a method to estimate the dose using amorphous silicon detectorpanel cone beam computed tomography (aSi-kVCBCT) for the OARs and targets in prostate radiotherapy and to comparewith the actual planned dose. Methods: The aSi-kVCBCT is used widely in radiotherapy to verify the patient positionbefore treatment. The advancement in aSi-kVCBCT combined with adaptive software allows us to verify the dosedistribution in daily acquired CBCT images. CBCT images from 10 patients undergoing radical prostate radiotherapywere included in this study. Patients received total dose of 65Gy in 25 fractions using volumetric modulated arc therapy(VMAT). aSi-kVCBCT scans were acquired before daily treatment and exported to smart adapt software for imageadaptation. The planning CT is adapted to daily aSi-kVCBCT images in terms of HU mapping. The primary VMATplans were copied on to the adapted planning CT images and dose was calculated using Anisotropic Analytic Algorithm(AAA). The DVH is then used to evaluate the volume changes of organs at risk (OAR), the actual dose received byOARs, CTV and PTV during a single fraction. Results: The normalized volume of the bladder and rectum rangedfrom 0.70–1.66 and 0.70–1.16 respectively. The cumulative mean Sorensen–Dice coefficient values of bladder andrectum were 0.89±0.04 and 0.79±0.06 respectively. The maximum dose differences for CTV and PTV were 2.5% and-4.7% and minimum were 0.1% and 0.1% respectively. Conclusion: The adapted planning CT obtained from dailyimaging using aSi-kVCBCT and SmartAdapt® can be used as an effective tool to estimate the volume changes anddose difference in prostate radiotherapy.