融合注意力机制与残差可形变卷积的肝肿瘤分割方法

Fusion of Attention Mechanism and Deformable Residual Convolution forLiver Tumor Segmentation

手术与化疗作为肝癌的主要治疗手段需要精确提取肝脏病变区域。针对目前肝肿瘤分割方法存在的小型肿瘤丢失、肿瘤边界分割模糊、分割严重错误等问题,提出一种融合注意力机制与残差可形变卷积的肝肿瘤分割方法。以U-Net为主干网络,在编码卷积层末尾增加一条带有反卷积与激活函数的残差路径,该路径与上层跳跃连接相连,解决池化与反卷积操作中的信息损失造成的小目标分割缺失与边界模糊问题;利用可形变卷积增强模型对肿瘤边界的特征提取能力;在跳跃连接层中添加一定数量的卷积层,弥补简单跳跃连接在特征融合时造成的语义空白;通过双注意力机制,模型更加关注肿瘤特征;采用混合损失函数,该函数在保证训练稳定的情况下解决类不平衡造成的分割性能下降的问题。在肝脏肿瘤公开数据集(LITS)上进行实验,所提方法的肿瘤分割Dice系数达85.2%,分割性能优于其他对比网络,能够达到辅助医疗诊断的要求。

Surgery and chemotherapy,as the main treatments for liver cancer,require accurate extraction for the liver lesion area.Therefore,to solve the problems of the current segmentation methods for liver tumors,such as the loss of small-sized tumors,fuzzy segmentation of tumor boundaries,and severe missegmentation,a new method for liver tumor segmentation based on the attention mechanism and deformable residual convolution is proposed.U-Net was used as the backbone network,and a residual path with deconvolution and activation function was added at the end of the encoding convolution layer to connect with the upper layer,thereby solving the problem of missing small target segmentation and fuzzy boundaries caused by information loss in pooling and deconvolution operations.Furthermore,a deformable convolution was used to enhance the model for extracting features of tumor boundaries.Several convolution layers were added to the skip connection layer to compensate for the semantic gaps caused by simple skip connections in feature fusion.The model pays more attention to tumor characteristics through the dual-attention mechanism.The mixed loss function was used to address the problem of segmentation performance degradation caused by a class imbalance under the condition of ensuring the stability of training.The experiment was carried out using the Liver Tumor Segmentation Challenge(LITS)dataset.The experimental results show that the Dice coefficient of tumor segmentation of the proposed method reaches 85.2%.Moreover,the proposed method has a better segmentation performance than other comparison networks,meeting the requirements of auxiliary medical diagnosis.