Meeting Banner
Abstract #1906

Detection of cerebral infarction and estimation of vascular territory via deep convolutional autoencode

Yuya Saito1,2, Akihiko Wada2, Shimpei Kato2, Koji Kamagata2, Masaaki Hori3, and Shigeki Aoki2
1Department of Radiological Sciences, Graduate School of Human Health Sciences, Tokyo Metropolitan University, Tokyo, Japan, 2Department of Radiology, Graduate School of Medicine, Juntendo University, Tokyo, Japan, 3Department of Radiology, Toho University Omori Medical Center, Tokyo, Japan

In the treatment of acute infarction, the detection of abnormal high signals in diffusion weighted images contributes to early diagnosis and treatment of infarction. In this study, we developed a deep learning neural network model via autoencoder (AE) to diagnosis brain infarction and predict vascular territory automatically from a DWI image. 1582 brain images including normal and abnormal brain which had infarctions were used as a training and test dataset. As a result, our model detected brain infarction and estimated vascular territory with high accuracy. It can be an effective indicator for diagnosing correctly infarction and predicting treatment effect.

This abstract and the presentation materials are available to 2020 meeting attendees and eLibrary customers only; a login is required.

Join Here