Evaluating a Deep-Learning System for Automatically Calculating the Stroke ASPECT Score
- Resource Type
- Conference
- Authors
- Jung, Su-min; Whangbo, Taeg-keun
- Source
- 2018 International Conference on Information and Communication Technology Convergence (ICTC) Information and Communication Technology Convergence (ICTC), 2018 International Conference on. :564-567 Oct, 2018
- Subject
- Bioengineering
Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
Fields, Waves and Electromagnetics
Power, Energy and Industry Applications
Signal Processing and Analysis
Transportation
Computed tomography
Lesions
Image segmentation
Biological neural networks
Medical diagnostic imaging
Stroke
ASPECT Score
Deep-Learning
Segmentation
Brain CT
- Language
The stroke is one of the leading causes of death around the world. It is a dangerous disease that results in a permanent disability. CT and MRI are representative imaging diagnostic tools for diagnosing the stroke. Particularly, CT has an advantage of examining the disease quickly. The Alberta Stroke Program Early CT Score (ASPECTS) is widely used as a tool to demonstrate the severity of the stroke based on CT images. However, it has a scoring variability issue among medical experts. This study proposed an object and automated ASPECT Score estimation system based on the image processing and deep learning technology for resolving the issue.