Name
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 integrates classical image processing and deep neural network for segmenting WHM from fluid attenuation inversion recovery (FLAIR) and T1 magnetic resonance (MR) images. In this system, a novel skip connection U-net (SC U-net) was proposed. In addition, an atlas-based method was introduced in the preprocessing stage to remove non-brain tissues (namely skull-stripping) and thus to improve the segmentation accuracy. Effectiveness of the proposed system was validated on a dataset of 60 paired images based on cross-scanner validation. Our experimental results revealed the effectiveness of the skull-stripping strategy. More importantly, compared to two existing state-of-the-art methods for segmenting WHM, including a U-net-like method and another deep learning method, the proposed SC U-net had a faster convergence, a lower loss and a higher segmentation accuracy. Both quantitative and qualitative analyses (via visual examinations) revealed the superior performance of our proposed SC U-net. The mean dice score of the proposed SC U-net was 78.36% which was much higher than those of a U-net-like method (74.99%) and an alternative deep learning method (74.80%). The software environment and model of the proposed system were made publicly accessible at Dockerhub. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/ACCESS.2019.2948476 | IEEE ACCESS |
Keywords | DocType | Volume |
Deep learning,white matter hyperintensity,skip connection,U-net | Journal | 7 |
ISSN | Citations | PageRank |
2169-3536 | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jiong Wu | 1 | 2 | 3.06 |
| Yue Zhang | 2 | 0 | 1.69 |
| Kai Wang | 3 | 0 | 1.69 |
| Xiaoying Tang | 4 | 8 | 8.79 |