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SDResU-Net: Separable and Dilated Residual U-Net for MRI Brain Tumor Segmentation
Jianxin Zhang1, Xiaogang Lv1, Qiule Sun2
1Key Lab of Advanced Design and Intelligent Computing (Ministry of Education), Dalian University, Dalian, China.
Background:
Glioma is one of the most common and aggressive primary brain tumors that endanger human health. Tumors segmentation is a key step in assisting the diagnosis and treatment of cancer disease. However, it is a relatively challenging task to precisely segment tumors considering characteristics of brain tumors and the device noise. Recently, with the breakthrough development of deep learning, brain tumor segmentation methods based on fully convolutional neural network (FCN) have illuminated brilliant performance and attracted more and more attention.
Methods:
In this work, we propose a novel FCN based network called SDResU-Net for brain tumor segmentation, which simultaneously embeds dilated convolution and separable convolution into residual U-Net architecture. SDResU-Net introduces dilated block into a residual U-Net architecture, which largely expends the receptive field and gains better local and global feature descriptions capacity. Meanwhile, to fully utilize the channel and region information of MRI brain images, we separate the internal and inter-slice structures of the improved residual U-Net by employing separable convolution operator. The proposed SDResU-Net captures more pixel-level details and spatial information, which provides a considerable alternative for the automatic and accurate segmentation of brain tumors.
Results And Conclusion:
The proposed SDResU-Net is extensively evaluated on two public MRI brain image datasets, i.e., BraTS 2017 and BraTS 2018. Compared with its counterparts and stateof- the-arts, SDResU-Net gains superior performance on both datasets, showing its effectiveness. In addition, cross-validation results on two datasets illuminate its satisfying generalization ability.
Insights
This study introduces SDResU-Net, a novel deep learning model for accurate brain tumor segmentation. The model significantly improves segmentation performance on MRI images, offering a valuable tool for glioma diagnosis and treatment.
Area of Science:
- Medical Imaging
- Artificial Intelligence
- Neuro-oncology
Background:
- Glioma is a common and aggressive primary brain tumor.
- Accurate tumor segmentation is crucial for diagnosis and treatment.
- Challenges include tumor heterogeneity and imaging noise.
Purpose of the Study:
- To develop a novel deep learning network for precise brain tumor segmentation.
- To improve the accuracy and efficiency of automated tumor segmentation in MRI scans.
Main Methods:
- Proposed SDResU-Net, a novel fully convolutional neural network (FCN) based on residual U-Net architecture.
- Integrated dilated and separable convolutions to enhance feature extraction and receptive field.
- Utilized separable convolution to process internal and inter-slice structures for better spatial information.
Main Results:
- SDResU-Net demonstrated superior performance compared to state-of-the-art methods on BraTS 2017 and BraTS 2018 datasets.
- The model effectively captured pixel-level details and spatial information for accurate segmentation.
- Cross-validation confirmed the model's robust generalization ability across datasets.
Conclusions:
- SDResU-Net offers a considerable alternative for automatic and accurate brain tumor segmentation.
- The proposed architecture enhances the capacity for local and global feature description.
- This method aids in improving diagnostic and therapeutic strategies for glioma patients.
