Ghose S, Mitra J, Oliver A, Martí R, Lladó X, Freixenet J, Vilanova J C, Comet J, Sidibé D, Meriaudeau F
Le2i CNRS-UMR 6306, Université de Bourgogne, Le Creusot, France.
Annu Int Conf IEEE Eng Med Biol Soc. 2012;2012:2335-8. doi: 10.1109/EMBC.2012.6346431.
Imaging artifacts in Transrectal Ultrasound (TRUS) images and inter-patient variations in prostate shape and size challenge computer-aided automatic or semi-automatic segmentation of the prostate. In this paper, we propose to use multiple mean parametric models derived from principal component analysis (PCA) of shape and posterior probability information to segment the prostate. In contrast to traditional statistical models of shape and intensity priors, we use posterior probability of the prostate region determined from random forest classification to build, initialize and propagate our model. Multiple mean models derived from spectral clustering of combined shape and appearance parameters ensure improvement in segmentation accuracies. The proposed method achieves mean Dice similarity coefficient (DSC) value of 0.96±0.01, with a mean segmentation time of 0.67±0.02 seconds when validated with 46 images from 23 datasets in a leave-one-patient-out validation framework.
经直肠超声（TRUS）图像中的成像伪影以及患者之间前列腺形状和大小的差异，给前列腺的计算机辅助自动或半自动分割带来了挑战。在本文中，我们建议使用从形状主成分分析（PCA）和后验概率信息导出的多个均值参数模型来分割前列腺。与传统的形状和强度先验统计模型不同，我们使用由随机森林分类确定的前列腺区域的后验概率来构建、初始化和传播我们的模型。从组合形状和外观参数的谱聚类导出的多个均值模型确保了分割精度的提高。在留一患者出验证框架中，用来自23个数据集的46幅图像进行验证时，所提出的方法实现了0.96±0.01的平均骰子相似系数（DSC）值，平均分割时间为0.67±0.02秒。
