• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文会议>Conference on Computer-Aided Diagnosis >Automatic classification of cardioembolic and arteriosclerotic ischemic strokes from apparent diffusion coefficient datasets using texture analysis and deep learning
【24h】

Automatic classification of cardioembolic and arteriosclerotic ischemic strokes from apparent diffusion coefficient datasets using texture analysis and deep learning

机译：使用纹理分析和深度学习从表观扩散系数数据集中自动分类心脏栓塞和动脉硬化性缺血性中风

获取原文
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Stroke is a leading cause of death and disability in the western hemisphere. Acute ischemic strokes can be broadly classified based on the underlying cause into atherosclerotic strokes, cardioembolic strokes, small vessels disease, and stroke with other causes. The ability to determine the exact origin of an acute ischemic stroke is highly relevant for optimal treatment decision and preventing recurrent events. However, the differentiation of atherosclerotic and cardioembolic phenotypes can be especially challenging due to similar appearance and symptoms. The aim of this study was to develop and evaluate the feasibility of an image-based machine learning approach for discriminating between arteriosclerotic and cardioembolic acute ischemic strokes using 56 apparent diffusion coefficient (ADC) datasets from acute stroke patients. For this purpose, acute infarct lesions were semi-atomically segmented and 30,981 geometric and texture image features were extracted for each stroke volume. To improve the performance and accuracy, categorical Pearson's x~2 test was used to select the most informative features while removing redundant attributes. As a result, only 289 features were finally included for training of a deep multilayer feed-forward neural network without bootstrapping. The proposed method was evaluated using a leave-one-out cross validation scheme. The proposed classification method achieved an average area under receiver operator characteristic curve value of 0.93 and a classification accuracy of 94.64%. These first results suggest that the proposed image-based classification framework can support neurologists in clinical routine differentiating between atherosclerotic and cardioembolic phenotypes.
机译：中风是西半球死亡和残疾的主要原因。急性缺血性中风可以根据潜在原因大致分为动脉粥样硬化性中风，心脏栓塞性中风，小血管疾病和其他原因引起的中风。确定急性缺血性卒中确切起源的能力与最佳治疗决策和预防复发事件高度相关。然而，由于相似的外观和症状，动脉粥样硬化和心脏栓塞表型的分化可能特别具有挑战性。这项研究的目的是使用来自急性中风患者的56个表观扩散系数（ADC）数据集，开发和评估基于图像的机器学习方法来区分动脉硬化性和心脏栓塞性急性缺血性中风的可行性。为此，将急性梗塞病变进行半原子分割，并为每个笔划量提取30,981个几何和纹理图像特征。为了提高性能和准确性，分类皮尔森的x〜2检验用于选择信息量最大的功能，同时删除冗余属性。结果，最终仅包含289个功能，无需进行自举即可训练深层多层前馈神经网络。提出的方法是使用留一法交叉验证方案进行评估的。提出的分类方法实现了接收机操作员特征曲线下的平均面积为0.93，分类精度为94.64％。这些最初的结果表明，所提出的基于图像的分类框架可以为神经科医师在动脉粥样硬化和心脏栓塞表型之间的临床常规区分中提供支持。

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