Related Experiment Video
Updated: Dec 20, 2025
Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
Published on: November 30, 2022
DCU-Net: Multi-scale U-Net for brain tumor segmentation
Tiejun Yang1,2, Yudan Zhou3, Lei Li3
1Key Laboratory of Grain Information Processing and Control (Henan University of Technology), Ministry of Education, China.
Background:
Brain tumor segmentation plays an important role in assisting diagnosis of disease, treatment plan planning, and surgical navigation.
Objective:
This study aims to improve the accuracy of tumor boundary segmentation using the multi-scale U-Net network.
Methods:
In this study, a novel U-Net with dilated convolution (DCU-Net) structure is proposed for brain tumor segmentation based on the classic U-Net structure. First, the MR brain tumor images are pre-processed to alleviate the class imbalance problem by reducing the input of the background pixels. Then, the multi-scale spatial pyramid pooling is used to replace the max pooling at the end of the down-sampling path. It can expand the feature receptive field while maintaining image resolution. Finally, a dilated convolution residual block is combined to improve the skip connections in the training networks to improve the network's ability to recognize the tumor details.
Results:
The proposed model has been evaluated using the Brain Tumor Segmentation (BRATS) 2018 Challenge training dataset and achieved the dice similarity coefficients (DSC) score of 0.91, 0.78 and 0.83 for whole tumor, core tumor and enhancing tumor segmentation, respectively.
Conclusions:
The experiment results indicate that the proposed model yields a promising performance in automated brain tumor segmentation.
