Liao Shu, Gao Yaozong, Shen Dinggang
Department of Radiology and BRIC, University of North Carolina at Chapel Hill, USA.
Med Image Comput Comput Assist Interv. 2012;15(Pt 3):385-92. doi: 10.1007/978-3-642-33454-2_48.
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.
自动前列腺分割在图像引导放射治疗中起着重要作用。然而，CT图像中的准确前列腺分割仍然是一个具有挑战性的问题，主要原因有三个：图像对比度低、前列腺运动大以及肠道气体导致的图像外观变化。本文提出了一种新的针对特定患者的前列腺分割方法来解决这三个问题。我们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补丁的表示方法，以有效区分属于前列腺和非前列腺区域的体素。（2）将新的基于补丁的表示方法与新的稀疏标签传播框架相结合来分割前列腺，基于稀疏表示可以有效去除补丁相似度低的候选体素。（3）采用在线更新机制，从先前治疗日扫描的治疗图像中获取更多特定患者的信息。所提出的方法在一个由24名患者共330幅图像组成的前列腺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，我们提出的方法在分割精度上比其他比较方法更高。
