[2J4] [General Session] 13. AI Application

[2J4-04] Segmentation of anatomical structures in chest X-ray pictures using U-Net

〇Kenji Kondo^1,2, Jun Ozawa¹, Masaki Kiyono^2,3, Shinichi Fujimoto³, Masato Tanaka³, Toshiki Adachi³, Harumi Ito³, Hirohiko Kimura³ (1. Advanced Industrial Science and Technology, 2. Panasonic Corporation, 3. University of Fukui)

Keywords:semantic segmentation, medical image, Computer-Aided Diagnosis, chest X-ray picture

We report a segmentation process of multiple anatomical structures in chest X-ray pictures, which is a key element of computer-aided diagnosis (CAD) systems, and its evaluation results. The segmentation process utilizes U-Net, that is, a type of fully convolutional network. The segmentation targets are a small region like a first thoracic vertebra and a line structure which is boundary between anatomical structures. In the evaluation, we achieve Dice index 0.91 as the segmentation accuracy for the first thoracic vertebra, and Dice index 0.71 through 0.81 as the segmentation accuracy for the line structures.