Influence of input image configurations on output of a convolutional neural network to detect cerebral aneurysms

Kazuhiro Watanabe, Hitomi Anzai, Norman Juchler, Sven Hirsch, Philippe Bijlenga, Makoto Ohta

研究成果: Conference contribution

抄録

Rupture of cerebral aneurysms is the main cause of subarachnoid hemorrhage, which can have devastating effects on quality of life. The identification and assessment of unruptured aneurysms from medical images is therefore of significant clinical relevance. In recent years, the availability of clinical imaging data has rapidly increased, which calls for computer assisted detection (CAD) systems. Previous studies have shown that CAD systems based on convolutional neural networks (CNN) can help to detect cerebral aneurysms from magnetic resonance angiographies (MRAs). However, these CAD systems require large datasets of annotated medical images. Thus, more efficient tools for processing and categorizing medical imaging data are required. Previous studies of CNN-based classification for medical images used various patch configurations of input data. These studies showed that classification accuracy was affected by the patch size or image representation. Thus, we hypothesize that the accuracy of CADs to detect cerebral aneurysms can be improved by adjusting the configuration of the input patches. In the present study, we performed CNN-based medical imaging classification for varying input data configurations to examine the relationship between classification accuracy and data configuration.

本文言語English
ホスト出版物のタイトルBiomedical and Biotechnology Engineering
出版社American Society of Mechanical Engineers (ASME)
ISBN(電子版)9780791859407
DOI
出版ステータスPublished - 2019
イベントASME 2019 International Mechanical Engineering Congress and Exposition, IMECE 2019 - Salt Lake City, United States
継続期間: 2019 11 112019 11 14

出版物シリーズ

名前ASME International Mechanical Engineering Congress and Exposition, Proceedings (IMECE)
3

Conference

ConferenceASME 2019 International Mechanical Engineering Congress and Exposition, IMECE 2019
国/地域United States
CitySalt Lake City
Period19/11/1119/11/14

ASJC Scopus subject areas

  • 機械工学

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