针对现有方法在腹部中小器官图像分割性能方面存在的不足,提出一种基于局部和全局并行编码的网络模型用于腹部多器官图像分割.首先,设计一种提取多尺度特征信息的局部编码分支;其次,全局特征编码分支采用分块Transformer,通过块内Transformer和块间Transformer的组合,既捕获了全局的长距离依赖信息又降低了计算量;再次,设计特征融合模块,以融合来自两条编码分支的上下文信息;最后,设计解码模块,实现全局信息与局部上下文信息的交互,更好地补偿解码阶段的信息损失.在Synapse多器官CT数据集上进行实验,与目前9种先进方法相比,在平均Dice相似系数(DSC)和Hausdorff距离(HD)指标上都达到了最佳性能,分别为83.10％和17.80 mm.
Abstract
Aiming at the shortcomings of existing methods in the image segmentation performance of small and medium-sized organs in the abdomen,we proposed a network model based on local and global parallel coding for multi-organ image segmentation in the abdomen.Firstly,a local coding branch was designed to extract multi-scale feature information.Secondly,the global feature coding branch adopted the block Transformer,which not only captured the global long distance dependency information but also reduced the computation amount through the combination of intra-block Transformer and inter-block Transformer.Thirdly,a feature fusion module was designed to fuse the context information from two coding branches.Finally,the decoding module was designed to realize the interaction between global information and local context information,so as to better compensate for the information loss in the decoding stage.Experiments were conducted on the Synapse multi-organ CT dataset,compared with the current nine advanced methods,the average Dice similarity coefficient(DSC)and Hausdorff distance(HD)indicators achieve the best performance,with 83.10％and 17.80 mm,respectively.
维普
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