Yoshikazu Uchiyama, Presenter: Nothing to Disclose

Takeshi Hara PhD, Abstract Co-Author: Nothing to Disclose

Toru Iwama, Abstract Co-Author: Nothing to Disclose

Hiroaki Hoshi MD, Abstract Co-Author: Nothing to Disclose

Junji Shiraishi, Abstract Co-Author: Nothing to Disclose

Hiroshi Fujita PhD, Abstract Co-Author: Nothing to Disclose

We improved automated detection method of lacunar infarcts on MRI images. Using combined pattern classifiers, the number of FPs was decreased from 0.71 to 0.38 per slice while keeping the same sensitivity of 96.8%. Our CAD scheme would assist radiologists in identifying lacunar infarcts on MR images.

The detection of asymptomatic lacunar infarcts on MR images is important because their presence indicates an increased risk of severe cerebral infarction. However, accurate identification of lacunar infarcts on MR images is difficult for radiologists because of the difficulty in distinguishing lacunar infarcts from enlarged Virchow-Robin spaces. Therefore, we have been developing CAD schemes for the detection of lacunar infarcts. Our previous method was applied to 132 patient cases and the sensitivity of 96.8% with 0.71 FP per slice was obtained. However, further elimination of FPs was remained as an issue to be solved for the clinical application. The purpose of this study is to improve our CAD scheme for detection of lacunar infarcts by use of combined pattern classifiers.

Uchiyama, Y, Hara, T, Iwama, T, Hoshi, H, Shiraishi, J, Fujita, H, Improvement of Automated Detection Method of Lacunar Infarcts on MR Images Based on Combined Pattern Classifiers.  Radiological Society of North America 2012 Scientific Assembly and Annual Meeting, November 25 - November 30, 2012 ,Chicago IL. http://archive.rsna.org/2012/12043889.html