The utility of a convolutional neural network for generating a myelin volume index map from rapid simultaneous relaxometry imaging

no code implementations • 24 Apr 2019 • Yasuhiko Tachibana, Akifumi Hagiwara, Masaaki Hori, Jeff Kershaw, Misaki Nakazawa, Tokuhiko Omatsu, Riwa Kishimoto, Kazumasa Yokoyama, Nobutaka Hattori, Shigeki Aoki, Tatsuya Higashi, Takayuki Obata

A CNN was trained to reconstruct RSRI-related metric maps into a myelin volume-related index (generated myelin volume index: GenMVI) map using the myelin volume index map calculated from magnetization transfer images (MTMVI) as reference.

A neural network model that learns differences in diagnosis strategies among radiologists has an improved area under the curve for aneurysm status classification in magnetic resonance angiography image series

no code implementations • 3 Feb 2020 • Yasuhiko Tachibana, Masataka Nishimori, Naoyuki Kitamura, Kensuke Umehara, Junko Ota, Takayuki Obata, Tatsuya Higashi

The constructed neural networks were trained to classify the aneurysm status of zero to five aneurysm-suspicious areas suggested by the CAD system for each image series, and any additional aneurysm areas added by the radiologists, and this classification was compared with the judgment of the annotating radiologist.

General Classification