Yang Xiaofeng, Rossi Peter J, Jani Ashesh B, Mao Hui, Curran Walter J, Liu Tian
Department of Radiation Oncology and Winship Cancer Institute.
Department of Radiology and Imaging Sciences and Winship Cancer Institute Emory University, Atlanta, GA 30322.
Proc SPIE Int Soc Opt Eng. 2016 Feb-Mar;9784. doi: 10.1117/12.2216396. Epub 2016 Mar 21.
We propose a 3D prostate segmentation method for transrectal ultrasound (TRUS) images, which is based on patch-based feature learning framework. Patient-specific anatomical features are extracted from aligned training images and adopted as signatures for each voxel. The most robust and informative features are identified by the feature selection process to train the kernel support vector machine (KSVM). The well-trained SVM was used to localize the prostate of the new patient. Our segmentation technique was validated with a clinical study of 10 patients. The accuracy of our approach was assessed using the manual segmentations (gold standard). The mean volume Dice overlap coefficient was 89.7%. In this study, we have developed a new prostate segmentation approach based on the optimal feature learning framework, demonstrated its clinical feasibility, and validated its accuracy with manual segmentations.
我们提出了一种用于经直肠超声（TRUS）图像的三维前列腺分割方法，该方法基于基于补丁的特征学习框架。从对齐的训练图像中提取患者特定的解剖特征，并将其用作每个体素的特征。通过特征选择过程识别出最稳健和信息丰富的特征，以训练核支持向量机（KSVM）。训练有素的支持向量机用于定位新患者的前列腺。我们的分割技术在对10名患者的临床研究中得到了验证。使用手动分割（金标准）评估了我们方法的准确性。平均体积骰子重叠系数为89.7%。在本研究中，我们基于最优特征学习框架开发了一种新的前列腺分割方法，证明了其临床可行性，并用手动分割验证了其准确性。
