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DCU-Net: Multi-scale U-Net for brain tumor segmentation.

Tiejun Yang^1,2, Yudan Zhou³, Lei Li³

¹Key Laboratory of Grain Information Processing and Control (Henan University of Technology), Ministry of Education, China.

Journal of X-Ray Science and Technology

|May 24, 2020

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This study introduces a novel multi-scale U-Net for brain tumor segmentation, achieving high accuracy in identifying tumor boundaries. The enhanced model improves diagnostic and treatment planning through precise automated segmentation.

Area of Science:

Medical imaging analysis
Artificial intelligence in healthcare
Neuro-oncology

Background:

Accurate brain tumor segmentation is crucial for diagnosis, treatment planning, and surgical navigation.
Existing segmentation methods may face challenges with tumor boundary delineation.

Purpose of the Study:

To enhance the accuracy of brain tumor boundary segmentation.
To develop an improved segmentation model using a novel multi-scale U-Net architecture.

Main Methods:

Proposed a novel dilated convolution U-Net (DCU-Net) incorporating multi-scale spatial pyramid pooling and dilated convolution residual blocks.
Pre-processed MR brain tumor images to address class imbalance.
Integrated multi-scale spatial pyramid pooling to expand receptive fields while preserving resolution.

Keywords:

Brain tumor segmentation DCU-Net U-Net dilated convolution multi-scale spatial pyramid pooling

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Main Results:

Achieved Dice Similarity Coefficients (DSC) of 0.91 (whole tumor), 0.78 (core tumor), and 0.83 (enhancing tumor) on the BRATS 2018 dataset.
Demonstrated improved ability to recognize tumor details through enhanced skip connections.

Conclusions:

The proposed DCU-Net model shows promising performance for automated brain tumor segmentation.
The novel architecture effectively improves tumor boundary segmentation accuracy.