Concurrent multimodality image segmentation by active contours for radiotherapy treatment planning
Concurrent multimodality image segmentation by active contours for radiotherapy treatment planning
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DOI：
10.1118/1.2799886
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发表时间：
2007-12-01
期刊：
影响因子：
3.8
通讯作者：
Deasy, Joseph O.
中科院分区：
文献类型：
--
作者：
El Naqa, Issam;Yang, Deshan;Deasy, Joseph O.
目前，多模态影像信息已被广泛应用于癌症患者的放射治疗计划中。作者正在研究利用所有可用的图像信息进行联合目标配准和分割的方法，包括多模态图像或来自同一模态的多图像集。特别是，作者开发了基于多值水平集可变形模型的变分方法，用于同时分割由共注册PET， CT或MR数据集组合组成的多模态图像的2D或3D。将组合的信息集成以定义整体生物物理结构体积。作者在三个患者数据集上演示了该方法，包括一个PET/CT的非小细胞肺癌病例，一个PET/CT的宫颈癌病例和一个CT和MRI的前列腺患者病例。CT、PET和MR幻像数据也被用于定量验证所提出的多模态分割方法。相应的Dice相似系数（DSC）为0.90+/-0.02 (p
Multimodality imaging information is regularly used now in radiotherapy treatment planning for cancer patients. The authors are investigating methods to take advantage of all the imaging information available for joint target registration and segmentation, including multimodality images or multiple image sets from the same modality. In particular, the authors have developed variational methods based on multivalued level set deformable models for simultaneous 2D or 3D segmentation of multimodality images consisting of combinations of coregistered PET, CT, or MR data sets. The combined information is integrated to define the overall biophysical structure volume. The authors demonstrate the methods on three patient data sets, including a nonsmall cell lung cancer case with PET/CT, a cervix cancer case with PET/CT, and a prostate patient case with CT and MRI. CT, PET, and MR phantom data were also used for quantitative validation of the proposed multimodality segmentation approach. The corresponding Dice similarity coefficient (DSC) was 0.90+/-0.02 (p