Name
Title | ||
|---|---|---|
Fully Automatic White Matter Hyperintensity Segmentation Using U-Net And Skip Connection |
Abstract | ||
|---|---|---|
White matter hyperintensity (WMH) is associated with various aging and neurodegenerative diseases. In this paper, we proposed and validated a fully automatic system which integrated classical image processing and deep neural network for segmenting WMH from fluid attenuation inversion recovery (FLAIR) and T1-weighed magnetic resonance (MR) images. A novel skip connection U-net (SC U-net) was proposed and compared with the classical U-net. Experiments were performed on a dataset of 60 images, acquired from three different scanners. Validation analysis and cross-scanner testing were conducted. Compared with U-net, the proposed SC U-net had a faster convergence and higher segmentation accuracy. The software environment and models of the proposed system were made publicly accessible at Dockerhub. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/EMBC.2019.8856913 | 2019 41ST ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC) |
Field | DocType | Volume |
Computer vision,White matter,Convolution,Segmentation,Computer science,Medical imaging,Image processing,Image segmentation,Software,Artificial intelligence,Artificial neural network | Conference | 2019 |
ISSN | Citations | PageRank |
1557-170X | 0 | 0.34 |
References | Authors | |
0 | 9 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yue Zhang | 1 | 0 | 1.69 |
| Jiong Wu | 2 | 2 | 3.06 |
| Wanli Chen | 3 | 13 | 5.42 |
| Yilong Liu | 4 | 34 | 4.65 |
| Junyan Lyu | 5 | 1 | 3.05 |
| Hongjian Shi | 6 | 0 | 0.68 |
| yifan chen | 7 | 19 | 9.10 |
| Ed X. Wu | 8 | 0 | 0.34 |
| Xiaoying Tang | 9 | 8 | 8.79 |