Abstract
Novel deep learning based network architectures are investigated for advanced brain tumor image classification and segmentation. Variations in brain tumor characteristics together with limited labelled datasets represent significant challenges in automatic brain tumor segmentation. In this paper, we present a novel architecture based on the U-Net that incorporates both global and local feature extraction paths to improve the segmentation accuracy. The results included in the paper show superior performance of the novel segmentation for five tumor regions on the large BRATs 2018 dataset over other approaches.
| Original language | English |
|---|---|
| Number of pages | 5 |
| Publication status | Published - 5 Sep 2019 |
| Event | 27th European Signal Processing Conference - A Coruna, Spain Duration: 2 Sep 2019 → 6 Sep 2019 http://eusipco2019.org/ |
Conference
| Conference | 27th European Signal Processing Conference |
|---|---|
| Abbreviated title | EUSIPCO 2019 |
| Country | Spain |
| City | A Coruna |
| Period | 2/09/19 → 6/09/19 |
| Internet address |
Keywords
- segmentation
- u-net
- tumor
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Kaewrak, K., Soraghan, J., Di Caterina, G., & Grose, D. (2019). Modified U-Net for automatic brain tumor regions segmentation. Paper presented at 27th European Signal Processing Conference, A Coruna, Spain.