Name
Title | ||
|---|---|---|
Deep Learning Model Integrating Dilated Convolution and Deep Supervision for Brain Tumor Segmentation in Multi-parametric MRI. |
Abstract | ||
|---|---|---|
Automatic segmentation of brain tumor in magnetic resonance images (MRI) is necessary for diagnosis, monitoring and treatment. Manual segmentation is time-consuming, expensive and subjective. In this paper we present a robust automatic segmentation algorithm based on 3D U-Net. We propose a novel residual block with dilated convolution (res dil block) and incorporate deep supervision to improve the segmentation results. We also compare the effect of different losses on the class imbalance problem. To prove the effectiveness of our method, we analyze each component proposed in the network architecture and we demonstrate that segmentation results can be improved by these components. Experiment results on the BraTS 2017 and BraTS 2018 datasets show that the proposed method can achieve good performance on brain tumor segmentation. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1007/978-3-030-32692-0_66 | Lecture Notes in Computer Science |
Keywords | DocType | Volume |
Deep learning,Brain tumor segmentation,Residual block,Deep supervision | Conference | 11861 |
ISSN | Citations | PageRank |
0302-9743 | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tongxue Zhou | 1 | 22 | 2.94 |
| Ruan Su | 2 | 559 | 53.00 |
| Haigen Hu | 3 | 44 | 8.47 |
| Stéphane Canu | 4 | 827 | 82.61 |