Yu Yanyan, Chen Yimin, Chiu Bernard
Department of Electronic Engineering, City University of Hong Kong, Kowloon, Hong Kong, China.
Department of Electronic Engineering, City University of Hong Kong, Kowloon, Hong Kong, China.
Comput Biol Med. 2016 Jul 1;74:74-90. doi: 10.1016/j.compbiomed.2016.05.002. Epub 2016 May 12.
Prostate segmentation from transrectal ultrasound (TRUS) images plays an important role in the diagnosis and treatment planning of prostate cancer. In this paper, a fully automatic slice-based segmentation method was developed to segment TRUS prostate images. The initial prostate contour was determined using a novel method based on the radial bas-relief (RBR) method, and a false edge removal algorithm proposed here in. 2D slice-based propagation was used in which the contour on each image slice was deformed using a level-set evolution model, which was driven by edge-based and region-based energy fields generated by dyadic wavelet transform. The optimized contour on an image slice propagated to the adjacent slice, and subsequently deformed using the level-set model. The propagation continued until all image slices were segmented. To determine the initial slice where the propagation began, the initial prostate contour was deformed individually on each transverse image. A method was developed to self-assess the accuracy of the deformed contour based on the average image intensity inside and outside of the contour. The transverse image on which highest accuracy was attained was chosen to be the initial slice for the propagation process. Evaluation was performed for 336 transverse images from 15 prostates that include images acquired at mid-gland, base and apex regions of the prostates. The average mean absolute difference (MAD) between algorithm and manual segmentations was 0.79±0.26mm, which is comparable to results produced by previously published semi-automatic segmentation methods. Statistical evaluation shows that accurate segmentation was not only obtained at the mid-gland, but also at the base and apex regions.
经直肠超声（TRUS）图像中的前列腺分割在前列腺癌的诊断和治疗规划中起着重要作用。本文开发了一种基于切片的全自动分割方法来分割TRUS前列腺图像。利用一种基于径向浅浮雕（RBR）方法的新方法确定初始前列腺轮廓，并在此提出一种假边缘去除算法。采用基于二维切片的传播方法，其中每个图像切片上的轮廓使用水平集演化模型进行变形，该模型由二进小波变换生成的基于边缘和基于区域的能量场驱动。图像切片上优化后的轮廓传播到相邻切片，随后使用水平集模型进行变形。传播过程持续进行，直到所有图像切片都被分割。为了确定传播开始的初始切片，在每个横向图像上单独对初始前列腺轮廓进行变形。开发了一种基于轮廓内外平均图像强度的方法来自我评估变形轮廓的准确性。选择获得最高准确性的横向图像作为传播过程的初始切片。对来自15个前列腺的336幅横向图像进行了评估，这些图像包括在前列腺的腺体中部、基部和尖部区域采集的图像。算法分割结果与手动分割结果之间的平均平均绝对差（MAD）为0.79±0.26mm，这与先前发表的半自动分割方法的结果相当。统计评估表明，不仅在腺体中部，而且在基部和尖部区域都获得了准确的分割结果。
