Name
Abstract | ||
|---|---|---|
With the continuous expansion of China's medical rigid demand and the imbalance between supply and demand caused by insufficient medical resources, this gap provides an entry point for the combination of the Internet and the medical industry. As a result, network medical care has gradually entered people's attention. Therefore, this paper designs a network-based ventricular segmentation system that will be trained. The ventricular segmentation model is placed in the cloud, and the user only needs to input the Cardiac Magnetic Resonance Image (CMRI) through the network terminal to obtain the detection result. The model introduces the Mask R-CNN algorithm based on deep learning into the research of nuclear magnetic image edge detection, trying to solve the problem of less image, difficult marking and low edge precision. Ventricular segmentation is performed through a deep learning network. Thereby it improves the accuracy of CMRI edge detection. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/DASC/PiCom/CBDCom/CyberSciTech.2019.00064 | 2019 IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech) |
Keywords | Field | DocType |
CMRI,Ventricular Segmentation,Machine Learning | Computer vision,Data modeling,Segmentation,Computer science,Edge detection,Medical imaging,Image segmentation,Artificial intelligence,Deep learning,The Internet,Cloud computing | Conference |
ISBN | Citations | PageRank |
978-1-7281-3025-5 | 0 | 0.34 |
References | Authors | |
1 | 8 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yilin Hu | 1 | 0 | 1.35 |
| Hang Yin | 2 | 0 | 0.34 |
| Binbin Yong | 3 | 0 | 0.34 |
| Yunshan Cao | 4 | 0 | 0.68 |
| Xing Zhou | 5 | 0 | 0.34 |
| Rui Zhou | 6 | 21 | 17.94 |
| Qingquan Lv | 7 | 0 | 0.34 |
| Mingsong Wang | 8 | 0 | 0.68 |