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

[2J4-01] Improvement of CT image segmentation by Deep Residual 3D U-Nets and 3D-CNNs

〇Keita Ninomiya¹, Yoshinobu Furuyama², Joji Ota², Hiroki Suyari¹ (1. Chiba University, 2. Department of Radiology, Chiba University Hospital)

Keywords:AI, Segmentation of CT, Deep learning

Segmentation of medical images with high precision and speed is an important task in many medical scenes. One such method for this task is GraphCut based on energy minimization problem. However, in GraphCut, it is difficult to perform segmentation completely and automatically if adjacent pixel values are similar. There are many methods for this problem, but most of them are not suitable in speed. In deep learning methods, automatic segmentation is possible because of its capability of capturing complicated features. In this research, we propose a model incorporating 3D U-Net extended with Residual Unit and 3DCNN for correcting segmentation results.