首页> 美国卫生研究院文献>BMC Systems Biology >Hierarchical combinatorial deep learning architecture for pancreas segmentation of medical computed tomography cancer images
【2h】

Hierarchical combinatorial deep learning architecture for pancreas segmentation of medical computed tomography cancer images

机译:用于医学计算机断层扫描癌症图像的胰腺分割的分层组合深度学习架构

代理获取
本网站仅为用户提供外文OA文献查询和代理获取服务,本网站没有原文。下单后我们将采用程序或人工为您竭诚获取高质量的原文,但由于OA文献来源多样且变更频繁,仍可能出现获取不到、文献不完整或与标题不符等情况,如果获取不到我们将提供退款服务。请知悉。

摘要

BackgroundEfficient computational recognition and segmentation of target organ from medical images are foundational in diagnosis and treatment, especially about pancreas cancer. In practice, the diversity in appearance of pancreas and organs in abdomen, makes detailed texture information of objects important in segmentation algorithm. According to our observations, however, the structures of previous networks, such as the Richer Feature Convolutional Network (RCF), are too coarse to segment the object (pancreas) accurately, especially the edge.
机译:背景技术从医学图像中对目标器官进行有效的计算识别和分割是诊断和治疗(尤其是有关胰腺癌)的基础。在实践中,腹部胰腺和器官外观的多样性使得对象的详细纹理信息在分割算法中很重要。但是,根据我们的观察,以前的网络(例如Richer Feature Convolutional Network(RCF))的结构过于粗糙,无法精确地分割对象(胰腺),尤其是边缘。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
代理获取

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