Name
Abstract | ||
|---|---|---|
Accuracy segmentation of brain structures could be helpful for glioma and radiotherapy planning. However, due to the visual and anatomical differences between different modalities, the accurate segmentation of brain structures becomes challenging. To address this problem, we first construct a residual block based U-shape network with a deep encoder and shallow decoder, which can trade off the framework performance and efficiency. Then, we introduce the Tversky loss to address the issue of the class imbalance between different foreground and the background classes. Finally, a model ensemble strategy is utilized to remove outliers and further boost performance. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1007/978-3-030-71827-5_3 | MICCAI |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Munan Ning | 1 | 8 | 2.87 |
| Cheng Bian | 2 | 4 | 2.44 |
| Chenglang Yuan | 3 | 0 | 2.03 |
| Yefeng Zheng | 4 | 1391 | 114.67 |