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SPA-UNet: A liver tumor segmentation network based on fused multi-scale features
Weikun Li1, Maoning Jia1, Chen Yang2
1School of Computer and Information Security, Guilin University of Electronic Technology, Guilin, Guangxi, 541000, China.
Abstract:
Liver tumor segmentation is a critical part in the diagnosis and treatment of liver cancer. While U-shaped convolutional neural networks (UNets) have made significant strides in medical image segmentation, challenges remain in accurately segmenting tumor boundaries and detecting small tumors, resulting in low segmentation accuracy. To improve the segmentation accuracy of liver tumors, this work proposes space pyramid attention (SPA)-UNet, a novel image segmentation network with an encoder-decoder architecture. SPA-UNet consists of four modules: (1) Spatial pyramid convolution block (SPCB), extracting multi-scale features by fusing three sets of dilated convolutions with different rates. (2) Spatial pyramid pooling block (SPPB), performing downsampling to reduce image size. (3) Upsample module, integrating dense positional and semantic information. (4) Residual attention block (RA-Block), enabling precise tumor localization. The encoder incorporates 5 SPCBs and 4 SPPBs to capture contextual information. The decoder consists of the Upsample module and RA-Block, and finally a segmentation head outputs segmented images of liver and liver tumor. Experiments using the liver tumor segmentation dataset demonstrate that SPA-UNet surpasses the traditional UNet model, achieving a 1.0 and 2.0% improvement in intersection over union indicators for liver and tumors, respectively, along with increased recall rates by 1.2 and 1.8%. These advancements provide a dependable foundation for liver cancer diagnosis and treatment.
Insights
This study introduces SPA-UNet, a novel deep learning model for improved liver tumor segmentation. The new network enhances accuracy in identifying liver and tumor boundaries, aiding liver cancer diagnosis.
Area of Science:
- Medical image analysis
- Artificial intelligence in oncology
- Deep learning for medical segmentation
Background:
- Accurate liver tumor segmentation is crucial for liver cancer diagnosis and treatment.
- Traditional U-shaped convolutional neural networks (UNets) face challenges in precise boundary delineation and small tumor detection, impacting segmentation accuracy.
Purpose of the Study:
- To enhance the accuracy of liver tumor segmentation using a novel deep learning architecture.
- To address limitations in current UNet models for segmenting liver tumors, particularly small ones and their boundaries.
Main Methods:
- Proposes Space Pyramid Attention (SPA)-UNet, an encoder-decoder network.
- Incorporates Spatial Pyramid Convolution Block (SPCB) for multi-scale feature extraction and Spatial Pyramid Pooling Block (SPPB) for downsampling.
- Utilizes an Upsample module and Residual Attention Block (RA-Block) in the decoder for precise localization.
Main Results:
- SPA-UNet achieved a 1.0% and 2.0% improvement in intersection over union (IoU) for liver and tumors, respectively, compared to traditional UNet.
- Demonstrated increased recall rates by 1.2% for liver and 1.8% for tumors.
- Experimental results on a liver tumor segmentation dataset validated the model's superior performance.
Conclusions:
- SPA-UNet significantly improves liver tumor segmentation accuracy and detection capabilities.
- The proposed model offers a dependable foundation for advancing liver cancer diagnosis and treatment planning.
- Enhanced segmentation performance contributes to more effective clinical decision-making in oncology.
