IEEE Trans Med Imaging. 2016 Jun;35(6):1395-407. doi: 10.1109/TMI.2015.2512606. Epub 2016 Jan 5.
In this paper, a fully automatic method is proposed to segment the kidney into multiple components: renal cortex, renal column, renal medulla and renal pelvis, in clinical 3D CT abdominal images. The proposed fast automatic segmentation method of kidney consists of two main parts: localization of renal cortex and segmentation of kidney components. In the localization of renal cortex phase, a method which fully combines 3D Generalized Hough Transform (GHT) and 3D Active Appearance Models (AAM) is applied to localize the renal cortex. In the segmentation of kidney components phase, a modified Random Forests (RF) method is proposed to segment the kidney into four components based on the result from localization phase. During the implementation, a multithreading technology is applied to speed up the segmentation process. The proposed method was evaluated on a clinical abdomen CT data set, including 37 contrast-enhanced volume data using leave-one-out strategy. The overall true-positive volume fraction and false-positive volume fraction were 93.15%, 0.37% for renal cortex segmentation; 83.09%, 0.97% for renal column segmentation; 81.92%, 0.55% for renal medulla segmentation; and 80.28%, 0.30% for renal pelvis segmentation, respectively. The average computational time of segmenting kidney into four components took 20 seconds.
本文提出了一种全自动方法，用于将临床 3D CT 腹部图像中的肾脏分割成多个部分：肾皮质、肾柱、肾髓质和肾盂。所提出的快速自动肾脏分割方法主要由两部分组成：肾皮质的定位和肾脏成分的分割。在肾皮质定位阶段，应用了一种充分结合三维广义霍夫变换（GHT）和三维主动外观模型（AAM）的方法来定位肾皮质。在肾脏成分分割阶段，提出了一种改进的随机森林（RF）方法，根据定位阶段的结果将肾脏分割成四个部分。在实现过程中，应用了多线程技术来加速分割过程。该方法使用留一法策略在一个临床腹部 CT 数据集上进行了评估，包括 37 个增强体积数据。肾皮质分割的总体真阳性体积分数和假阳性体积分数分别为 93.15%和 0.37%；肾柱分割的总体真阳性体积分数和假阳性体积分数分别为 83.09%和 0.97%；肾髓质分割的总体真阳性体积分数和假阳性体积分数分别为 81.92%和 0.55%；肾盂分割的总体真阳性体积分数和假阳性体积分数分别为 80.28%和 0.30%。将肾脏分割成四个部分的平均计算时间为 20 秒。
