LEARNING-BASED DETECTION OF FLOW DIVERTERS IN CEREBRAL IMAGES

被引：0

作者：

Zhu, Ying J. ^{[1
]}

机构：

[1] Temple Univ, Elect & Comp Engn, Philadelphia, PA 19122 USA

来源：

2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI) | 2015年

关键词：

flow diverter detection; machine learning; brain aneurysm; stenting; INTRACRANIAL ANEURYSMS; ENDOVASCULAR TREATMENT; STENT;

D O I：

暂无

中图分类号：

R318 [生物医学工程];

学科分类号：

0831 ;

摘要：

We propose a machine learning-based method to automatically detect flow diverters in cerebral C-arm CT images. An appearance detector is learned to generate hypotheses of a flow diverter's location in a volumetric image. A probabilistic framework incorporating a local appearance and shape model is developed to trace the flow diverter. Promising results have been obtained on clinical data. The proposed method provides a potential solution to the automation of cerebral aneurysm treatment workflow and in particular the post-operative assessment of flow diverter placement.

引用

页码：1122 / 1125

页数：4

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