Sparse patch based prostate segmentation in CT images.
Sparse patch based prostate segmentation in CT images.
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DOI：
10.1007/978-3-642-33454-2_48
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发表时间：
2012
期刊：
影响因子：
--
通讯作者：
Shen, Dinggang
中科院分区：
文献类型：
--
作者：
Liao, Shu;Gao, Yaozong;Shen, Dinggang
自动前列腺分割在图像引导放射治疗中发挥着重要作用。然而，CT图像中精确的前列腺分割仍然是一个具有挑战性的问题，主要由于三个问题：图像对比度低、前列腺运动大以及肠气引起的图像外观变化。在本文中，提出了一种新的患者特异性前列腺分割方法来解决这三个问题。我们的方法的主要贡献在于以下几个方面：（1）在判别特征空间中设计了一种新的基于块的表示，以有效地区分属于前列腺和非前列腺区域的体素。 （2）新的基于补丁的表示与新的稀疏标签传播框架集成以分割前列腺，其中基于稀疏表示可以有效地去除具有低补丁相似性的候选体素。 (3)采用在线更新机制，从先前治疗日扫描的治疗图像中捕获更多患者特定信息。该方法已在包含 24 名患者、总共 330 张图像的前列腺 CT 图像数据集上进行了广泛评估。它还与几种最先进的前列腺分割方法进行了比较，实验结果表明，我们提出的方法可以比其他比较方法实现更高的分割精度。
Automatic prostate segmentation plays an important role in image guided radiation therapy. However, accurate prostate segmentation in CT images remains as a challenging problem mainly due to three issues: Low image contrast, large prostate motions, and image appearance variations caused by bowel gas. In this paper, a new patient-specific prostate segmentation method is proposed to address these three issues. The main contributions of our method lie in the following aspects: (1) A new patch based representation is designed in the discriminative feature space to effectively distinguish voxels belonging to the prostate and non-prostate regions. (2) The new patch based representation is integrated with a new sparse label propagation framework to segment the prostate, where candidate voxels with low patch similarity can be effectively removed based on sparse representation. (3) An online update mechanism is adopted to capture more patient-specific information from treatment images scanned in previous treatment days. The proposed method has been extensively evaluated on a prostate CT image dataset consisting of 24 patients with 330 images in total. It is also compared with several state-of-the-art prostate segmentation approaches, and experimental results demonstrate that our proposed method can achieve higher segmentation accuracy than other methods under comparison.
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影响因子：
10.6
作者：
Rousseau F;Habas PA;Studholme C
通讯作者：
Studholme C
影响因子：
5.7
作者：
Jenkinson, M;Bannister, P;Smith, S
通讯作者：
Smith, S
DOI：
10.1007/978-3-642-23626-6_70
发表时间：
2011
期刊：
Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
影响因子：
--
作者：
Li W;Liao S;Feng Q;Chen W;Shen D
通讯作者：
Shen D
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
影响因子：
3.8
作者：
Feng, Qianjin;Foskey, Mark;Shen, Dinggang
通讯作者：
Shen, Dinggang
影响因子：
10.9
作者：
Chen, Siqi;Lovelock, D. Michael;Radke, Richard J.
通讯作者：
Radke, Richard J.