语义扩散对齐的多尺度感知医学图像分割方法
Semantic Diffusion Alignment-based Multi-scale Perception for Medical Image Segmentation
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摘要: 有效的多尺度特征表示对于准确地分割医学图像中大小不一的病变区域具有重要意义. 针对现有方法未能充分挖掘不同目标的多尺度信息导致难以实现精确分割的问题, 提出一种语义扩散对齐的多尺度感知医学图像分割方法. 首先从局部和全局2个角度探索多尺度上下文信息的感知能力, 构建多尺度编码器和解码器. 其中, 多尺度编码器利用局部多尺度自注意力机制和全局重调整来提取多尺度特征, 以捕获图像中不同目标的信息, 多尺度解码器则通过上采样恢复空间分辨率并保留细节信息, 得到更准确的分割结果; 为了进一步增强特征语义表示, 提出语义扩散对齐模块, 实现了低级特征与高级特征的语义对齐, 获得更具鉴别性的融合特征. 在Synapse和ACDC数据集上的实验结果表明, 所提方法的平均Dice相似系数分别达到82.42%和92.25%, 性能优于大多数现有方法. 该方法的相关代码链接为https://github.com/MiniCoCo-be/MSPSN.Abstract: Effective multi-scale feature representation is crucial for accurately segmenting lesions of varying sizes in medical images. Addressing the challenge of existing methods failing to fully exploit multiscale information for different targets, a multi-scale perception segmentation network is proposed. It first explores the perceptual ability of multiscale contextual information from both local and global perspectives, constructing multiscale encoders and decoders. The multi-scale encoder utilizes a local multi-scale self-attention mechanism and global fine-tuning to extract features at multiple scales, capturing information from different targets in the image. The multi-scale decoder, through upsampling, restores spatial resolution while preserving detailed information, leading to more precise segmentation results. To further enhance feature semantic representation, a semantic diffusion alignment module is introduced, achieving semantic alignment between low-level and high-level features to obtain more discriminative fused features. Experimental validation on multiple medical image datasets of different modalities demonstrates the outstanding performance of the proposed method, surpassing most current medical image segmentation methods and achieving more accurate and robust segmentation results.