Liver Semantic Segmentation Method Based on Multi-Channel Feature Extraction and Cross Fusion

Abstract

Semantic segmentation plays a critical role in medical image analysis, offering indispensable information for the diagnosis and treatment planning of liver diseases. However, due to the complex anatomical structure of the liver and significant inter-patient variability, the current methods exhibit notable limitations in feature extraction and fusion, which pose a major challenge to achieving accurate liver segmentation. To address these challenges, this study proposes an improved U-Net-based liver semantic segmentation method that enhances segmentation performance through optimized feature extraction and fusion mechanisms. Firstly, a multi-scale input strategy is employed to account for the variability in liver features at different scales. A multi-scale convolutional attention (MSCA) mechanism is integrated into the encoder to aggregate multi-scale information and improve feature representation. Secondly, an atrous spatial pyramid pooling (ASPP) module is incorporated into the bottleneck layer to capture features at various receptive fields using dilated convolutions, while global pooling is applied to enhance the acquisition of contextual information and ensure efficient feature transmission. Furthermore, a Channel Transformer module replaces the traditional skip connections to strengthen the interaction and fusion between encoder and decoder features, thereby reducing the semantic gap. The effectiveness of this method was validated on integrated public datasets, achieving an Intersection over Union (IoU) of 0.9315 for liver segmentation tasks, outperforming other mainstream approaches. This provides a novel solution for precise liver image segmentation and holds significant clinical value for liver disease diagnosis and treatment.

Keywords: feature extraction; feature fusion; liver segmentation.

Figure 1

U-Net architecture.

Figure 2

Structure diagram of multi-scale convolutional attention (MSCA).

Figure 3

Structure of the ASPP module.

Figure 4

CCT structure.

Figure 5

CCA structure.

Figure 6

Improved network architecture.

Figure 7

Dataset partitioning framework.

Figure 8

Examples of data augmentation.

Figure 9

Heatmap comparison between U-Net and TAMU-Net encoders across encoding layers.

Figure 10

Heatmap comparison between U-Net and TAMU-Net decoders across decoding layers.

Figure 11

Comparative visualization of segmentation results using different methods.

Figure 12

Comparative visualization of segmentation results using different methods.

Figure 13

Comparative visualization of heatmaps using different methods.

Figure 14

Comparative visualization of heatmaps using different methods.