Department of Radiology and Imaging Sciences, Emory University School of Medicine, Atlanta, GA, 30329, USA.
The Wallace H. Coulter Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, GA, 30329, USA.
Med Phys. 2017 Oct;44(10):5128-5142. doi: 10.1002/mp.12396. Epub 2017 Jul 18.
Accurate segmentation of the prostate on ultrasound images has many applications in prostate cancer diagnosis and therapy. Transrectal ultrasound (TRUS) has been routinely used to guide prostate biopsy. This manuscript proposes a semiautomatic segmentation method for the prostate on three-dimensional (3D) TRUS images.
The proposed segmentation method uses a context-classification-based random walk algorithm. Because context information reflects patient-specific characteristics and prostate changes in the adjacent slices, and classification information reflects population-based prior knowledge, we combine the context and classification information at the same time in order to define the applicable population and patient-specific knowledge so as to more accurately determine the seed points for the random walk algorithm. The method is initialized with the user drawing the prostate and non-prostate circles on the mid-gland slice and then automatically segments the prostate on other slices. To achieve reliable classification, we use a new adaptive k-means algorithm to cluster the training data and train multiple decision-tree classifiers. According to the patient-specific characteristics, the most suitable classifier is selected and combined with the context information in order to locate the seed points. By providing accuracy locations of the seed points, the random walk algorithm improves segmentation performance.
We evaluate the proposed segmentation approach on a set of 3D TRUS volumes of prostate patients. The experimental results show that our method achieved a Dice similarity coefficient of 91.0% ± 1.6% as compared to manual segmentation by clinically experienced radiologist.
The random walk-based segmentation framework, which combines patient-specific characteristics and population information, is effective for segmenting the prostate on ultrasound images. The segmentation method can have various applications in ultrasound-guided prostate procedures.
在超声图像上准确分割前列腺在前列腺癌诊断和治疗中有许多应用。经直肠超声（TRUS）已常规用于引导前列腺活检。本文提出了一种用于三维（3D）TRUS 图像的前列腺半自动分割方法。
所提出的分割方法使用基于上下文分类的随机游走算法。由于上下文信息反映了患者特定的特征和相邻切片中的前列腺变化，而分类信息反映了基于人群的先验知识，因此我们同时结合上下文和分类信息，以便定义适用的人群和患者特定的知识，从而更准确地确定随机游走算法的种子点。该方法以用户在中腺切片上绘制前列腺和非前列腺圆开始，然后自动在其他切片上分割前列腺。为了实现可靠的分类，我们使用新的自适应 k-均值算法对训练数据进行聚类，并训练多个决策树分类器。根据患者的特定特征，选择最合适的分类器并结合上下文信息，以便定位种子点。通过提供种子点的准确位置，随机游走算法提高了分割性能。
我们在一组前列腺患者的 3D TRUS 体积上评估了所提出的分割方法。实验结果表明，与临床经验丰富的放射科医生的手动分割相比，我们的方法的 Dice 相似系数达到 91.0%±1.6%。
结合患者特定特征和人群信息的基于随机游走的分割框架对于在超声图像上分割前列腺是有效的。该分割方法可在超声引导的前列腺手术中有多种应用。
