Segmenting CT prostate images using population and patient-specific statistics for radiotherapy
Segmenting CT prostate images using population and patient-specific statistics for radiotherapy
复制标题
使用放射治疗的人群和患者特定统计数据分割 CT 前列腺图像
DOI：
10.1118/1.3464799
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发表时间：
2010-08-01
期刊：
影响因子：
3.8
通讯作者：
Shen, Dinggang
中科院分区：
文献类型：
--
作者：
Feng, Qianjin;Foskey, Mark;Shen, Dinggang
本文提出了一种新的变形模型，使用人口和患者特定的统计分割前列腺CT图像。在所提出的方法中有两个新颖之处。首先，一种改进的尺度不变特征变换（SIFT）的局部描述符，这是更独特的比一般的强度和梯度特征，被用来描述图像的特征。其次，在线训练的方法是用来建立形状的统计，准确地捕捉患者内的变化，这是更重要的比患者间的变化，前列腺分割在临床放射治疗。实验结果表明，该方法具有较好的鲁棒性和准确性，适合于临床应用。
This paper presents a new deformable model using both population and patient-specific statistics to segment the prostate from CT images. There are two novelties in the proposed method. First, a modified scale invariant feature transform (SIFT) local descriptor, which is more distinctive than general intensity and gradient features, is used to characterize the image features. Second, an online training approach is used to build the shape statistics for accurately capturing intra-patient variation, which is more important than inter-patient variation for prostate segmentation in clinical radiotherapy. Experimental results show that the proposed method is robust and accurate, suitable for clinical application.