Automatic Detection and Segmentation of Liver Tumors in Multi- phase CT Images by Phase Attention Mask R-CNN
- Resource Type
- Conference
- Authors
- Hasegawa, Ryo; Iwamoto, Yutaro; HAN, Xianhua; Lin, Lanfen; Hu, Hongjie; CAI, Xiujun; Chen, Yen-Wei
- Source
- 2021 IEEE International Conference on Consumer Electronics (ICCE) Consumer Electronics (ICCE), 2021 IEEE International Conference on. :1-5 Jan, 2021
- Subject
- Aerospace
Bioengineering
Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
Engineered Materials, Dielectrics and Plasmas
Engineering Profession
Fields, Waves and Electromagnetics
General Topics for Engineers
Geoscience
Nuclear Engineering
Photonics and Electrooptics
Power, Energy and Industry Applications
Robotics and Control Systems
Transportation
Image segmentation
Computed tomography
Conferences
Liver
Feature extraction
Computer aided diagnosis
Consumer electronics
multi-phase CT image
detection
segmentation
liver tumor
Mask R-CNN
phase attention
- Language
- ISSN
- 2158-4001
In computer-aided diagnosis of liver tumors, tumor detection and segmentation are essential pretreatment steps. In this study, we proposed a phase attention mask R-CNN based method for simultaneous detection and segmentation of liver tumors in multi-phase CT images. Each feature of the triple phase image is selectively extracted by the attention network for each scale. The segmentation accuracy (Dice value) is about 0.60 ~ 0.66 for single-phase CT images, and the accuracy can be improved to about 0.77 by the proposed method using attention network with multi-phase CT images.