• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Medical image analysis >A supervised learning framework of statistical shape and probability priors for automatic prostate segmentation in ultrasound images
【24h】

A supervised learning framework of statistical shape and probability priors for automatic prostate segmentation in ultrasound images

机译：超声图像中自动前列腺分割的统计形状和概率先验的有监督学习框架

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Prostate segmentation aids in prostate volume estimation, multi-modal image registration, and to create patient specific anatomical models for surgical planning and image guided biopsies. However, manual segmentation is time consuming and suffers from inter-and intra-observer variabilities. Low contrast images of trans rectal ultrasound and presence of imaging artifacts like speckle, micro-calcifications, and shadow regions hinder computer aided automatic or semi-automatic prostate segmentation. In this paper, we propose a prostate segmentation approach based on building multiple mean parametric models derived from principal component analysis of shape and posterior probabilities in a multi-resolution framework. The model parameters are then modified with the prior knowledge of the optimization space to achieve optimal prostate segmentation. In contrast to traditional statistical models of shape and intensity priors, we use posterior probabilities of the prostate region determined from random forest classification to build our appearance model, initialize and propagate our model. Furthermore, multiple mean models derived from spectral clustering of combined shape and appearance parameters are applied in parallel to improve segmentation accuracies. The proposed method achieves mean Dice similarity coefficient value of 0.91. ±. 0.09 for 126 images containing 40 images from the apex, 40 images from the base and 46 images from central regions in a leave-one-patient-out validation framework. The mean segmentation time of the procedure is 0.67. ±. 0.02. s.
机译：前列腺分割有助于前列腺体积估计，多模式图像配准，并为手术计划和图像引导活检创建患者特定的解剖模型。但是，手动分段非常耗时，并且存在观察者之间和观察者内部的变化。直肠超声的低对比度图像以及诸如斑点，微钙化和阴影区域之类的成像伪影的存在阻碍了计算机辅助的自动或半自动前列腺分割。在本文中，我们提出了一种基于前列腺癌的分割方法，该方法基于建立在多分辨率框架中从形状和后验概率的主成分分析得出的多个平均参数模型。然后，利用优化空间的先验知识修改模型参数，以实现最佳的前列腺分割。与形状和强度先验的传统统计模型相比，我们使用从随机森林分类确定的前列腺区域的后验概率来构建外观模型，初始化和传播模型。此外，并行应用从组合形状和外观参数的光谱聚类得到的多个均值模型，以提高分割精度。所提出的方法实现了平均骰子相似系数值为0.91。 ±。在“一无所有”验证框架中，包含顶点40幅图像，根部40幅图像和中心区域46幅图像的126幅图像为0.09。该过程的平均分割时间为0.67。 ±。 0.02。 s。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号